{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "共享单车数据分析及用户预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入所需要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('C:/Users/lj/Desktop/第一周作业/day.csv')\n",
    "data = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据结构\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011/1/1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011/1/2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011/1/3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011/1/4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011/1/5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant    dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011/1/1       1   0     1        0        6           0   \n",
       "1        2  2011/1/2       1   0     1        0        0           0   \n",
       "2        3  2011/1/3       1   0     1        0        1           1   \n",
       "3        4  2011/1/4       1   0     1        0        2           1   \n",
       "4        5  2011/1/5       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据前五行\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#数据总计\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据描述\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b19c798668>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制用户分布直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=20, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "考察系数之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3gfeFEI7AR0BlRVGqARMURTkMbAGGKorioSjKVWCToiieiqJUBy4AvdNs1wVoALRB3ygHGA4cTFl/ZvogQoi+QohgIUTwnyvWpF/8dhFm5qXt4ROCGmO6cWrs6jcWCUDBtJcx5fOYwY4Tl2lbtwL+479gTj8fRq70R6dLXe9seDR5bawpY+HGcLpQpvPS9ZAm7ArmTL0vOd/iO+4fPEOpWd9YOIJphvS9tJkpk5aVlRp3d1eOHAmmfn1vjh07wZQpI18rp5WVmlo1q+HTrgetvT9jxI/fUrbsuzRr2oCaNapy9Mg2goP8adasAe+Wesdk/Re9hkZN2lOnbiva+HSjf/9eNGxQN0vrv7hMxuu2bt2cu3diOHnqrNnX3Od/g3mnRE0uXgyjU8fMjds0/5bK2vGsWLEsEyYMZ+DAHzO1z1eR1fdUeh/V6cwXrfsxesAEvh07ELcSrrki1yd1PqV36/6MGTCRb8YOeO1cb8v5ae5eYO46/KaZq5uMYnm2b8A71UqzZ+EWAAqXKEaxMm6MrNefEfX6Ue69KpSuU9GC4czMS3/cVGpUxdx4NGkwj+dNxLbP92CXD1RqrMpX4cmaBTwc/RWqoi5YN2ppZoOvms38tcyIWo3a1Z37P33Dw+njyPf1UEQ+faeYLuYu9wZ9QXzfz8j7QStEwYw/EElvXm5qnB9XFCVC0f+i/Gn0Q0/uA4nAn0KIj4GMnqlVEUIcFEKcBboCldMs+1dRFF3K+PRMfRtDUZSFiqLUVhSldp8epl82eps81sRhl6bxaufiyJPo1KEJ1vnzUrBCcZr9PRKfY7MoXLMMDZd9b/hSaHYpVjA/0QmpPeW3Ex6aDFv552goXjXKAlC9lAtPk7UkPHpiWL7jZBitapXN1pzPNLHYuKb2Rti4OJF0O86ojDb+AcqzZADurt6FXdV3LZohMlKDu3tqA8LNzcUwtMFcGbVajb19AeLiMh6CEhsbz6NHj9m8eQcAmzb54eFR5bVz7vTfx+PHT4iNjedg4FGqVauEEIKVqzZQ29OL2p5eVK7SiHHjf6Vdu1YEB/kTHORPrZrViIzUUDzN63R3czH09Gk0+v/evRvL5s3b8fT0MN1/hGk9RWlum5QpnqaeHBzsiYuLJ8JcHUfd5r33atOmjRdhl4+yetU8mjZ9n+XLZhttU6fTsX7DFj76yDuT9RSNu7uLcc6odDlfcDzd3JxZt24hffoM5vr1m5na56u4o7lLUdeihumiLkWIuZ353rXnZaNuajh55DTlqpTJplyFibkd84I1Ms516shpyr5mrrfl/LytuYNLmh79Yq5FuROd+XrLLgnRsRRKc48q5OLEvTumQ3jKv1+VlgM/ZkGfaSSnXG+rt6xD+Kkwnj1+yrPHTzm//zSlaljunqDExSAcU58uqByLoCQYnwO6uLsknTwMWi3K3Wh0mluoi7mjxN1Fe+OKfkiMTkfSiUOoS1oumy7mLqrCqeeByqkIurgYkzLPjgWCVovudjS6yFuoXI2/P6bExZJ8MxzrStUslu2N0umy9y+H5KbGedrBWFrASlGUZKAO8Df6ceY7Mlh3GTBQUZSqwFggbwbbNfc5+D8t7vQ1CpRyJl/xIqis1bzTrh4R/icMy5MePGFTlX741v0W37rfEnPyCgd7zSDuzPUXbPX1VX6nGDfvJhAZe4+kZC07T16mcVXjDwQuhfJz7LL+i5fXouN4lqSlUH5bAHQ6hV2nwmiVjUNaAB6dDiNPKRdsihdFWFvh2K4B8f5BRmWsi6b2OBT08iTxSkT6zbyW4OAQypQpRcmSxbG2tqZjRx+2bt1lVGbr1l2GYSQff9ya/fsPv3S7fn67ady4PgBNm75v9MXJV7HFdycN3q+LWq3G1jYvderU4OLFMPbuC+Tjj9oYviBaqFBB3nnHjc2bdxga7CdOnsF3qz+dOrXDxsaGkiWLU6ZMKY4HncLOzpb8+fUf3OzsbGnRvDHnz18y2X9Q8GmjeurcqR1bt/qnqyd/unfvCMAnn3izb/8hw/zOZvY9cuQUSr1bm7Ll6tG121fs23eInr0GAVC6dEnDdtt4t+DSpStkxvPjWaJE6vH08zM+nn5+u+na9RNAfzwPHNAfTwcHezZtWsqoUdM4csT0F44s6cLpixQv5YZLcWesrK1o3q4ZB/1f/r4CKOCQH2sbawAcCtlTzbMK1y/feMlamXPx9EXc0+T6oF0zAv2PvFKuqp5VCH/NXG/L+Xnu1AXeebc4bu+4YG1tRev2Ldi3M+C1tmkJN0KuUqSkM07uRVBbq6np8x5n0v16l3vlknw6qQ8L+kzjYex9w/z4qBjK1K2ESq1CZaWmbN2KRFvw+qu9dhG1sxuiiDOorbCu11TfEE8j+cQhrCrqOwtEfntUzu7o7mrQXruEyFcAUcABAKtKNdBFWuYcAEgOu4ja1R1VMWewsiJPo2YkHT9kVObZ0UCsquq/OyPsHVC5FkcXHYXKqQikfFdF5MuPdcUqaCNvWSyb9Ppy9T9CJITID9gpirJNCHEUeH73ewCk/YZlAUAjhLBG33P+skFn6dfPVkNHTyHo1BkSEu7zQftufNW7O5/4WPDx1gsoWh3BI5bR5K8fEGoV19Ye4P7lSKoO/YS4kOtE+p984fo+x2Zhnd8WlY0V7i1rs6/LFJNfenkVVmoVwzs0pv+8Leh0OtrVq0QZFyfm+R2l0jtFaVL1XQa3b8i4tXtZve8UCMHYrs0Nj0BPXI2kWMH8uBd2eO0sL6TVcXPkIsr/NRpUKmLW7SHx8i1ch3ThccgVEnYFUewLbwp6eaJotSQnPOT6t79bNoJWy7ff/oyv70rUajXLl6/jwoXLjBo1mBMnzuLnt4tly9axZMkszp8PIC4ugR49BhrWv3TpEAUKFMDGxhofn5a0adONixfDGDlyMkuWzOKXX0YTExNH377fvzDHqpVzadyoPoULOxJ+LZix46Zjba1v6CxctJKLF6+w038fp07uRqfTsWTJGkMjetSYaWzftgaVSpCUlMygQSO4edP4fRQaepmNG305G7KPZK2WQd+MQKfTUaxYETZuWAzoH/evXfsvO/33m62nb74diZ/fX6hVKpYtX0do6GVGjx7CiRMhbN26iyVL17Js2WwuhAYSH59A125fGfa9YaMvZ9LtOyNCCJYsnoW9fX4QgrNnQhmQySEmWq2W774bha/vipTjuZ4LF8L4+efBnDx5Bj+/3SnHcybnzh0gPj6B7t31x7Nfv56ULl2S4cO/ZvjwrwHw8enO3buxTJz4I507t8POzpYrV46ydOlaJk6clalM5nPqmDFyNrP+moZKpWLruu1cvxzO/4Z8zoWQSwTuOkzF6uWZsng8BRzy06BFffp8/zldm31OybIl+GHKYHSKgkoIVs5ZY/RrKq9Dq9Uxc+Tv/PrXVNQqtSFXnyG9uBhymcBdh6lQvTyTF4+jgEN+3m9Rnz7f96Jbsy8oUbYEw6Z8Z8i1ygK5csv5mZmcE3+czsK1s1GpVfyzxperl64zcFhfzodcYN/Og1TxqMhvS6dhX7AATbwaMmDo/2jXWP/0eMXmBZQqUwK7fLbsOeXLqO8mcGj/sdfKBKDT6lg/agkDVvyEUKs4un4/0WEReH/XkZtnr3F29wna/9iNPHZ56T1P/xWx+MgYFvzvF05tO0q596rw087pKIrChQOnObfnxfe0rIXT8WTF7+QbOhVUKpICtqOLvEGej3uhvX6J5FNHSD4bhFXV2uSfsgR0WhLXLkR5qP8AkbhmAfmGTwcB2vAwnu3zs2A2LY/mz8J+7HRQqXi6exvam+HYdv2C5LCLJB0/TNLJ41jX8MRh7nLQ6Xi89A+UB/ex8qhNgS++Qj9+SPDkn3Vob1x72R5zJSUHx4VnJ5GVsXoW37kQDxVFyS+EaAIMURSlTcr8OUAwsBPYjL4nXADTFUVZLoR4H1iEvle8A+AFDANuAGeBAoqi9BJCLAO2KoqyMd3+rNH3whcGlpkbd/5cUsy1nB+UZ8bGaj/ndIQMtV9aP6cjZOjcFxn/RndOahh74uWFckhyLv6pqtz8KMxKnTv7PjwcLTvsypJUufiInojN3JORN620g8vLC+WQprYlczpChiY2zPkhPeYkJyTndIQXcvI9kKtO0qdXj2ZrGy1P6Xo58npz9O6hKEr+lP/uB/anmT8wTTGT361TFOUQxj+l+EfKX/pyvTLYXxLwwSsHlyRJkiRJknJWDo4Lz065acy5JEmSJEmSJP2/ljufu0qSJEmSJEnSi/xHx5zLnnNJkiRJkiRJyiVkz7kkSZIkSZL09snFP1jwOmTPuSRJkiRJkiTlErLnXJIkSZIkSXr7yDHnkiRJkiRJkiRlJ9lzLkmSJEmSJL195O+cS5IkSZIkSZKUnWTPuSRJkiRJkvT2+Y+OOZeN85fYWO3nnI5gVocz43M6QoZsXRvmdIQMPVj1ZU5HMEvXPSinI7yVVKrc+/BPUZScjmBWGyvXnI6QITUipyNkqHBRu5yOYJadyL238Ujd45yOkKFte5xzOoJZ2lx8DgB0y+kA/0/k3rNakiRJkiRJkjLyHx1zLhvnkiRJkiRJ0ltHUeQ/QiRJkiRJkiRJUjaSPeeSJEmSJEnS2+c/+oVQ2XMuSZIkSZIkSbmE7DmXJEmSJEmS3j7/0S+Eyp5zSZIkSZIkScolZM+5JEmSJEmS9PaRY84lSZIkSZIkScpOsudckiRJkiRJevvo5O+cS5IkSZIkSZKUjWTj3EJcmlTD++AvtDk0g4oDfTIsV9y7Dl2iVuNYrRQANoXy02zDCDqELabWxJ5vKq7ByEm/0sj7U9p365dt+2jp1YTz5wK4GBrIsKEDTJbb2Njw1+o/uBgayOFAX0qUcDcs+2HYQC6GBnL+XABeLRob5i9aOIOoiBBOn9pjtK3q1Stz6KAvwUH+HD2yDc/aHq+U+VBYFO1mbcFn5maWBJw3Wa5JeESfJbvpPHcbHef4cfByJABnI2LoNHeb/m+OH3tDb73S/tPz8mrCubMHCA0NZOgQ83W4etU8QkMDCTyYWoeOjgXx37meuNhLzJo1wWidcWOHcfXKceJiL2Upi6WPZ548eThyaCsngncRcnovo0d9byj/Vf9eXAwNJPlZJE5OhbKU06tFE86e2U/o+YMMGfKV2ZyrVs4j9PxBDgZsMaqznTvXERtzkVkzxxvK29rm5d9/lnEmZB+nTu5mwvjhWcqTVosWjTlzZh/nzwdkmG3lyrmcPx9AQMDmdNnWEhNzgZkzxxmt06GDD0FBOzl5cjcTJ/70ytnSerdxNfrt/YX+B2ZQv7/pda1m1w/4384p9Nk2iR4bR1G4rBsArtXfpc+2Sfq/7ZMo37K2RfI8V6pxNf639xe+PDCDemZyeXRtxhc7J/P5tol03fgzTmVdjZbbuzoxOPRP6vRtbdFcADUa12TevvnMD1jIJ191MFnetk975uyZx287f2fcmokUcStiWDZ6xVhWn13LyKWjLJ4LoHrjGszYO5eZB/6gbf+PTZa37tOWX3b/ztQdsxjx1zgKp2Qr7FaEiVtnMHnbTH7ZNZvmXVtaNFeNxjWZs+8P5gUs4GOzddaO2XvmMnPnbMaumWBUZz+vGMOqs2sYkU11lpvv7S5NqtH24C+0OzSDyi/I9o63J92iVhlla77hJzqH/YnnxB7Zku2NUXTZ+5dDZOPcAoRKUGtSL/Z3nca2JsMo0a4+9ik3qbSs8uWlXO+WxJy4YpinTUzizC8bOD3urzcZ2aB96xbM/3XCywu+IpVKxezfJtLGpxtVqzelc+f2VKxY1qjMF593IT7+HhUqNWDW7EVMnjQCgIoVy9KpUzuqeTTDu01Xfp89CZVK/5ZdsWI93m26muxvyqQRjJ/wK7U9vRg7djpTJo/IcmatTsdk3yDm9mjKpq/bsONMOFfv3DMqs+jAObyqvMO6Aa2Z0qkBk3yDAChTtCB/9WvF+gGtmduzGeO3HCNZ+3onuEql4rffJuDTtjvVqzelc+d2VKxgXIeff/4p8Qn3qFSpAbNnL2JSSuMsMfEpY8b+wg/Dx5tsd6vfbt5v0CbLWSx9PJ8+fUpzr07Uqt2CWrW9aOnVhLp1agJw+EgQLT/8lPDwrH3IeV5nbduGPJ+QAAAgAElEQVT1oLpHMzp3akeF9HXW61MSEhKoVLkhs3//k4kTUuts7NjpDB9uel7MnLWAatWbUqfuh9R/z5OWXk2ylCtttnbteuLh8QGdOrU1ydarV2cSEu5RuXIjfv/9TyZM+DFNthkMHz7RqLyjY0EmT/6JDz/sQs2azSlWrDBNm76f5WxpCZWg1fherO05jQXNh1G5bX1D4/u5c5sPs6jlcP5s/RNH5m+l+Uj9OXnnUgSLfUbyZ+ufWNtzGh9O+gKhtsztRqgEXuN7sr7nNBY1H0altvVMGt+hm4+wpOWPLG09gmPz/fhgZDej5R+M6sq1/SEWyZOWSqXiywn9GdtzNAM/+IqGbRtTvGxxozLXz19lsPd3fNPyaw77BdLrp88Ny/5ZsIlZ3/1q8VwAQqXi8/FfMrXnOIY0/5r32jbEray7UZnw89cY0eZ7fmj1Lce2HeazH/WNyvg78Yz++Ad+bP0dI9sNo23/TyhUNGsfljOiUqnoO6Ef43uOYdAHA2jQthHu6ers2vlrDPEezHctB3HY7xA90tTZv9laZ7n33i5UgjqTerK36zR8mwyjZLt6OKQ7D55nK9+7JXfTZQv5ZSMnc6jdIb2cbJxbgGON0jwMv82jm3fRJWm5ufko7i1rmZSrNqwDF+ZtRfv0mWGe9slTYo5fRvs06U1GNqjtURUH+wLZtv06njW4ejWc69dvkpSUxPr1m2nrY9zr0tbHi5UrNwDw999+NGvaIGV+S9av38yzZ88ID7/F1avh1PGsAcDBwGPExSeY7E9RFAqkvB57hwJEaW5nOfO5iFiKOxXA3bEA1lZqWlYtwf4Lxo1DATxK1B+zh4nPKFLAFgBbGyusUhohz5K1CESW95+ep6eHSR36+HgZlfFJW4eb/GiaUoePHz/h8OEgEhOfmmz3+PGTREffyVKW7Dqejx49BsDa2gora2sURQHg9Onz3LgRkaWMYKbONmwxX2erNgKwaZOfoTFrqLOnxnX25EkiBw4cASApKYnTp87i5u7y2tk2bPA1m22VIds2k2xPnyYalS9V6h3Cwq4TExMHwN69gbRv/2GWs6Xl6lGauPDbJNzSX9dCfY9SroXxde3ZwyeG/7e2y2P4/+TEZygpH0rVeaxJOZwW4eJRmvjw29xLk6vsS3IppAYo61WLhJt3iUl52mVJZT3KER2u4fbN2yQnJXPQN4A6XvWMypw9cpZnKefjpVOXcHIpbFh25lAIT9Jkt6QyHmWJDtdw59ZttEnJHPENpHaLukZlQo+c41mi/v505dQlHF2cANAmJZP8LBkAaxtrhOr1r2vPlfUoiyZNnQX6BlDHyzjXuTR1dvnUJZxScgGcPXQm2+osN9/bnWqU5kH4bR6mZAvPIFv1YR0InbcVXZoc2idPuZuD7Q6L0umy9y+H5GjjXAiRTwjhJ4QIEUKcE0J0FkLUEkIcEEKcEELsFEK4pJT9nxAiKKXs30IIu5T5HVPWDRFCBKTMyyuEWCqEOCuEOCWEaJoyv5cQYpMQYocQIkwIMc0Sr8PO2ZHHUbGG6ceaOGxdjHsVClUpgZ2rE1G7T1lil28NVzdnbkVEGaYjIjW4ujpnWEar1XLv3n2cnArh6mpmXTfjddMbPGQ0UyeP5PrVIKZN+ZkRIydnOfOd+09wdrAzTBdzsOPOA+OLf79m1fALuY7XL5sYuHI/w71TH9ufvRXDx7O30mGOHyPb1jE01l+Vm6sLEbc0hunIyGhc3VzSlXEmIkJfRqvVcu/+/SwPA8mM7DqeKpWK4CB/NJFn2LMngONBr3eepN9XZKQGt/Q5XZ2JSJPz/v0Hma4zBwd7vL2bs2/foVfKFpEum6trsdfKdvXqDcqVK02JEu6o1Wp8fLxwdzftRcuKAs6OPNCkXtfua+Io4GyaoVaPFnwV8Csf/NiFnaOXp74Gj9L03TWVvjunsGPEEkNj/XUVcC7EA02cYfpBBrlq9mjOlwEzaPrjp+wevQIAa9s81OvfhsBZmyySJT0nZydiou4apmM1MTgVc8qwfIvOXpzYdyJbsqRXyNmRWE2MYTpWE0shZ8cMyzfp3JyQ/ScN044uhZm6YxZzjv7JlvmbiL8Tb5Fcjs5OxEQZ53pRnTXv3IKTb6jOcvO93c65EI+jUs+Dx5o47Mxky+fqSOTu0280m/T6crrnvBUQpShKdUVRqgA7gN+BDoqi1AKWAM+f325SFMVTUZTqwAWgd8r8UUDLlPltU+YNAFAUpSrQBVguhMibsswD6AxUBToLIYyfnwFCiL5CiGAhRPCex1fSLzZlrhMhbVeRENQY041TY1e/fFv/MUKYVo6SrhvNfJnMrZvel3178P3QMZQq7cn3Q8eyaMGMLCbGqJfNkDHd9I4z4bStWRr/oR8zp3sTRv59GJ1Ov17V4oXZNKgNq79sxeKA8zxNer1vk5uphkzWoQW7K7Own1c5njqdjtqeXpQoVRvP2jWoXLn8G8hpul5m6kytVrNyxRzmzl3K9es3sylb1o5nQsI9Bg0awcqVc9mzZyM3bkSQnJyc5WwvYy7DiRW7mNdoMHunrKXB1+0N86NOX2Vhix9Y0vZn3vuqLeo81hZKYe7Amc46uWI3Cxp9z/4pa3kvJVeDwR8T9OcOkh6bPknKtmgZHLfGHzWhTLUy/LPg7+zJko7Zp3gZvKUafNSYd6uWwXfBP4Z5cZoYfmj1Ld816kejT5riUNjBMrmy8F5v/FETSlcrw78LsufDlYncfG/P4DqbdnntMd04MfY/PnRFjjnPFmeB5kKIqUKIhkBxoAqwSwhxGhgJPB8UV0UIcVAIcRboClROmX8IWCaE+B+gTpnXAFgJoCjKReAGUC5l2R5FUe4pipIIhAIl0odSFGWhoii1FUWp/YFdmZe+iMeaOOxcUz/p27k48iQ6dciFdf68FKxQnGZ/j8Tn2CwK1yxDw2XfG76c8V8WGaGheJoePHc3FzTphpqkLaNWq3FwsCcuLp7ISDPrRr14mEqP7h35559tAGzc6IunZ9a/EFrM3o7oe48N07fvPTYMW3nunxNX8aryDgDV3ynC02QdCelu+O8WdcDWxoord0yH32RFRKQG9+KpPeVubs5ooqJNy6QMsVCr1TjY2xMX93r7NSe7j+e9e/c5EHD4lcZyG2VIty83NxeTIU6RkdGG3mW1Wo29fYFM1dm8eVO5cuU6v89Z/MrZ3NNl02juZFgms9m2bdtNo0btaNLkI8LCrnHlSvgr5XvuQXQcBdIMH7B3ceTh7YwznN9yhHJepl/8jL0SxbMnTylazt3MWq+aK7XHt4CLIw9uZ9yLG7rlKGW99I/7XT3K0PTHT+kfOJPaX7Sk/oC21OzZwiK5QN/rW9g19cuKTi6FibsTZ1KueoPqdBzYmYm9xxuGi2S3uOhYoyE0Ti5OxN82zVbl/Wq0H9iB6X0mmc0WfyeeiMu3KF+nkkVyxWpiKOxqnMtcnVVrUJ0OAzsxufeEN1Znufners+Weh7os6WeB9b58+JQwZ0Wf4+g/bGZFK5ZmibLBv+/aHf8F+Ro41xRlMtALfSN9MnAJ8B5RVE8Uv6qKoryfDDmMmBgSm/4WCBvyjb6oW/EFwdOCyGcMP9597m0LSgtFvit97jT1yhQypl8xYugslbzTrt6RPinPnZLevCETVX64Vv3W3zrfkvMySsc7DWDuDPXX3fXuV5Q8GnKlClFyZLFsba2plOndvhu9Tcq47vVn+7dOwLwySfe7Nt/yDC/U6d22NjYULJkccqUKfXS4Q5Rmts0blQfgGZNGxB2Jet1XNnNiZuxD4iMf0hSspadZ2/QuIJxw8KloB3HruobyNfu3ONZspZC+fIQGf/Q8AXQqISH3Ii5j2vBfFnOkFZwcIhJHW7dusuozNatu1Lr8GNv9u/P+nCLzMiO41m4sCMODvYA5M2blw+aNeTSpauvlVNfZyVTc3Zsa77Ouul/GeLjTNbZmDFDcbAvwPdDxrxmttQ67NjRx2y2boZsrdm///BLt1ukiL4RUbCgA337dmfp0jWvnBEgKuQajqWccUi5rlXyqcflXcbDCQqVTB2OU7aZB/Hh+nPCoXgRwxdA7d0K4/SuCwkRd7EEjZlcV3adNCqTNleZNLlWdxzPHw2+448G3xG8ZCdH5m7h5HLjun8dYSGXcSnlStHixbCytqKhTyOO7zpmVKZU5XfpP3kgE3uP517svQy2ZHlXQ8JwLuVCkeJFUVtbUd+nASd2HTcqU7JyKfpM/orpvSdxP002R2cnrPPYAJDPPh/la1dAczUKSwgLCTOqswY+jQhKl0tfZwOY9IbrLDff22PTZSvZrh4R/qnnQdKDJ2ys0p9/637Hv3W/I+bkVfb3+vW/1+74j445z9F/hEgI4QrEKYqySgjxEOgLFBFC1FcU5YgQwhoopyjKeaAAoEmZ1xWITNlGaUVRjgHHhBA+6BvpASll9gohygHvAJeAmtnxOhStjuARy2jy1w8ItYpraw9w/3IkVYd+QlzIdSL9T75wfZ9js7DOb4vKxgr3lrXZ12UK98Ms/2Ulc4aOnkLQqTMkJNzng/bd+Kp3dz7xsdzPZGm1Wr75diTb/P5CrVKxbPk6QkMvM2b0EIJPhLB16y6WLF3L8mWzuRgaSHx8Ap910/+0XGjoZTZu9OVsyD6StVoGfTMCXcrJsmrlXBo3qk/hwo6EXwtm7LjpLF22ln79hvLrr+OwsrLiaWIi/fsPy3JmK7WK4W1q03/5XnQ6hXY1S1OmWEHm7QmhkqsTTSq6M7hVLcZtPsrqwxdBCMZ+XB8hBKdu3GFJQChWahUqAT+28aRQvrwv3+lL6vDbb3/Gb+tqVGoVy5etI/TCZUaPGsKJk/o6XLp0LcuW/kZoaCDxcQl0657683yXLx3B3r4ANjbWtPVpibf3Z1y4GMbkSSPo3Lk9dna2XLsaxNKlaxg/4cW/epAdx9PFpRhLFs9CrVahUqnYuNEXv227ARg44AuGfP8Vzs5FOHViN9t37OXLfkMzXWdbfVehVqtZtnwdFy5cZtSo7zl54gxb/XaxdNlali6ZRej5g8TFJdC9R+rPQl66dBj7Avo68/FpiXebrjx48IAfhw/i4sUwjh3dDsAf85exdOnaVzqevr4rUavVLDdkG8yJE2fx89vFsmXrWLJkFufPBxAXl0CPHgPTZDtEgTTZ2rTpxsWLYcyYMYaqVfW9mZMmzeLKK3wwTUvR6tg5ahldVvyASq0iZP0BYsIiaTT4EzRnrhO2+yS1e3pRqkEVdElantx/xJbB8wEoXrs8733lgy5Ji6Lo2DFyKU/iH75WnrS5/Ectp/OKYQi1ijMpuRqm5Lqy+yS1enpRokFldElaEu8/wm/wAovs+2V0Wh0Lf57PmJXjUKlV7Fm3i1uXb/LZ4K5cORvG8V3H+XzEF9ja5WXYH/qf4oyJusvE3vpfU5q0cSrupd3Jmy8vi48tY87Q2ZwKePH9IyvZlo1axI8rRqNSq9m/fjcRYbfoMLgL189c4cTuID77qRd57fLyzTz9dTM26i7T+0zCrYw73UZ+jqIoCCHYunAzty7dsFiuRT/PZ/TKsSl1tptbl2/SJaXOgnYdp+eIz8lrl5ehKXV2N+ouk3vrf01p4sYpuKXU2aJjS5k7dDanAywz/js339sVrY6gEcv54C/9eXB17QHuXY6kWkq2iJdka39splG2vV2mcC/MMh+4pNcnsmNcaqZ3LkRL4BdAByQB/YFkYDbggP7DwyxFURYJIfoDw9APUTkLFFAUpZcQYhNQFn1v+R7gWyAPMB99r3wyMFhRlH1CiF5AbUVRBqbsfyswXVGU/RllXOPaNecq6AU6nDH9abzcwta1YU5HyNCDVV/mdASzHLovzOkIGdLl4DXiZdSqnB6ZlzFL/FJPdvi5WO49P9W5tM4AjiiWHyZmCXYi9/5D34lK7v3XGzsl2+d0BLO0ufgcAOgWtSpXBUw8uDJbb1B5G3bPkdebo2e1oig7gZ1mFjUyU/YP4A8z803/JQVIBHqZKbsM/fCY59NZ+5FnSZIkSZIkScpGufcjtyRJkiRJkiRlQMnFT2deh2ycS5IkSZIkSW+fHPzSZnbKvQM2JUmSJEmSJOn/GdlzLkmSJEmSJL19cvAfCspOsudckiRJkiRJknIJ2XMuSZIkSZIkvX3kmHNJkiRJkiRJkrKT7DmXJEmSJEmS3j5yzLkkSZIkSZIkSdlJ9pxLkiRJkiRJb5//6Jhz2Th/ifZL6+d0BLNsXRvmdIQMPYk6mNMRMpRb600lRE5HyFDuTQbaXHxhLpg3X05HMGtmfFBOR8iQTlFyOkKGXOwcczqCWVfuReV0hAx95lwnpyNkqGmZyJyOYNbTh7JZJsnGuSRJkiRJkvQ2kmPOJUmSJEmSJEnKTrLnXJIkSZIkSXr75OKhja9D9pxLkiRJkiRJUi4he84lSZIkSZKkt4/sOZckSZIkSZIkKTvJnnNJkiRJkiTp7SN/rUWSJEmSJEmSpOwke84lSZIkSZKkt48ccy5JkiRJkiRJUnaSPeeSJEmSJEnS20eOOZckSZIkSZIkKTvJxrmFHAq9QbsJK/EZt4Ilu4JNlmviHtBn9iY6T11Dxyl/cfB8OAB+QZfoNHWN4a/GN79zMeLuK2Vo6dWE8+cCuBgayLChA0yW29jY8NfqP7gYGsjhQF9KlHA3LPth2EAuhgZy/lwAXi0aG+YvWjiDqIgQTp/aY7St6tUrc+igL8FB/hw9sg3P2h6vlPlFRk76lUben9K+Wz+Lb/u5t6XOvLyacO7sAUJDAxk6xHzO1avmERoaSOBB45zDhg4gNDSQc2cP0CJNzkGD+nD61B5OndzNyhVzyJMnT+aznAvgQmggQzOos9Wr/+BCaCCH0tXZsGEDuRAayLlzAUZZwi4f5dTJ3Ya6ee7nnwcTfj2Y4CB/goP8adWqWaYyguWPrbu7K7v9N3D2zH5CTu/l64G9M50lvWbNG3L0xA6On97FoO/6mslmzZ9LZ3H89C527t1A8XfcjJa7ubsQHnWKAV9/YZjXt38PDh7dSuAxP778qmeuytZvQC8Cj/lx8OhWFi75lTx5bLKc64PmDTl2cifBp3fzzWBzuWxYvGwWwad3s2vvRrO5bmpOM3BQ6nGzdyjAspW/c/TEDo4G78Czzutfxxo0rcfWQ+vZfnQjfb7uYbK8Vj0PNuxaTkjkIbzaGL+fF6yZxZHLu5m7asZr5wDwatGEs2f2E3r+IEOGfGWy3MbGhlUr5xF6/iAHA7YYzgFHx4Ls3LmO2JiLzJo53lDe1jYv//6zjDMh+zh1cjcTxg+3SM4qjT2YtGc2U/bPoXX/j0xfR28fJuyaxbjtvzJ09Wic3IoYli2+up6x26Yzdtt0Bi2yTJ60bOrUofCqFRT+azX5un5msty2VSuKbvkXp8V/4rT4T2y9vQ3L8vfri9OypTgtW0reZk0tni3ve564blqK6+bl2Pf61GR5Ph8v3PdsxGXNfFzWzCd/+w8ByFO7umGey5r5vHNkG7ZN3rN4vjdCp8vevxwiG+cWoNXpmLxhP3P7tWXTT13ZceIyVzVxRmUW+QfhVaMs637owpSerZi0YT8A3p7lWf9DF9b/0IWJ3Vvg6mhPBfciZvbyYiqVitm/TaSNTzeqVm9K587tqVixrFGZLz7vQnz8PSpUasCs2YuYPGkEABUrlqVTp3ZU82iGd5uu/D57EiqV/q2xYsV6vNt0NdnflEkjGD/hV2p7ejF27HSmTB6R5cwv0751C+b/OsHi233ubakzlUrFb79NwKdtd6pXb0rnzu2oWME45+eff0p8wj0qVWrA7NmLmDTxJ33OCvqcHh7NaOPTjdmzJ6JSqXB1dWbAgC+oV9+bGjWbo1ar6dSpbabrzMenG9WqN+XTDOosIf4eFSs14LfZi5iUps46d2pHdY9mtElXZwDNW3SktqcX9eq3Ntreb7MXUdvTi9qeXuzYsTfTdWbpY5ucnMzQYWOpWq0J7zfwoX//XibbzGy2qTNG0/mT//G+Z2s+7tCGcuVLG5Xp2qMjCQn3qOPRgvlzlzF67FCj5RMm/8SeXQGG6QoVy9K9Zye8mnag8Xtt8WrZlHdLl8gV2ZxdivG/L7vTvPHHNKzXBpVKxUefeJMVKpWKaTPG0OnjPtT3/JBPOrShfPkyRmW69ehAQsJ9ans054+5SxkzzjjXpCkjjHIBTJ42kj27A6hXqxUN6/tw6dLVLOUyl3PElKH0++xb2jb8lNYfeVG6XCmjMprI24z4Zjx+m/xN1l8ybxU/DhzzWhnSZvnttwm0bdeD6h7N6NypHRXSXzd6fUpCQgKVKjdk9u9/MnGC/rqRmPiUsWOnM3y46fV35qwFVKvelDp1P6T+e5609GryWjmFSkX3cf9jZq+JjGjxLXXbNsC1jLtRmZuh1xnnM4xRHw4mePtROv3Y3bDsWeIzRrcewujWQ5j9vymvlcWESoX9d98QP/QHYnr0JO8HzVCXMD2vnuzdR2zvPsT27sMTPz8A8tSrh3XZcsT27kNcv/7k+/RThJ2dRbM5/vA1d77+iahPepOvVVOsS71jUuyR/340Xfqh6dKPh/9uB+BpcIhh3u0vh6JLTCTx6AnLZXuTFF32/uWQ/9eNcyGE2hLbOXfjNsWLFMS9sAPWVmpa1izH/rPXjPcFPEp8BsDDxKcUsc9nsp3tJy7Tqla5V8pQx7MGV6+Gc/36TZKSkli/fjNtfVoalWnr48XKlRsA+PtvP5o1bZAyvyXr12/m2bNnhIff4urVcOp41gDgYOAx4uITTPanKAoF7AsA+t6nKM3tV8r9IrU9quKQso/s8LbUmaenh0lOHx8vozI+aXNu8qNpSk4fHy+TnJ6e+t5BK7UVtrZ5UavV2NrZoslEnvR1tm79ZnzS1ZlPBnXm49OSdRnUmaVlx7GNjr7DqdPnAHj48BEXL4bh5uqc5Ww1a1fj+rUb3Ai/RVJSEv/87ceH3s2Nynzo/QFr1/wDwJZ/d9CwSf00y5pzI/wWly5eMcwrV740J4JCePIkEa1Wy+FDx/Fu0yJXZAOwsrIib8p7zc7OlujoO1nKVStdrk1/+/Fhmw+MyrT2bs7avzYBsPnfHTRKk6t1m+aEh9/i4oUww7wCBfLz3nuerFyufw8kJSVx/96DLOVKr2rNSty6HkHEjSiSkpLZ9u8umrZqZFQm6paGy6FXUMz0yh07GMyjh49fK8NzJteNDVvMXzdWbQRg0yY/mjZ9H4DHj59w+HAQiU+fGpV/8iSRAweOAPr6On3qLG7uLq+V812PMty5Ec3dW7fRJiVz3DeQGl6eRmUuHjnHs5T759VTlynk7PRa+8ws64oV0EZGotVoIDmZxD17ydvg/Uytqy5ZgmchIaDVoiQmknT1Cnnq1rFYNpsq5UmOiCI5Up/t0c792DbJXLa07Jo3IvFQEEri05cXlt6Y/3TjXAgxXgjxTZrpiUKIQUKIfUKIv4CzltjPnYRHOBfMb5guVjA/d+49NCrT78O6+AVfwuvnJQyc78vwDo3Tbwb/k2F8WPPVGueubs7ciogyTEdEanBN13BIW0ar1XLv3n2cnArh6mpmXbcXNzoGDxnN1MkjuX41iGlTfmbEyMmvlDsnvS115ubqQsQtjWE6MjIaVzeXdGWciYjQpOa8n5LTzcUwHyAyIho3VxeioqKZOWsBV68c4+aNk9y/94Ddu417Fc1xdXMmIs3rjozUmDRQM6ozfUbjdZ/XmaIobN+2hmNHt9Ont/FTh6/6f87JE7tYtHAGBQs6vDRj+gxg+WNbooQ7HtWrcOz4qUzlScvFpRhREdGG6aioaFxci5mUiUxzPO/ff4CjYyHs7GwZ9N3/+GXKHKPyF0LDqP9+bQo5FsTWNi/NvRrj+gqNpuzIFq25zdzfF3P6/H7Ohx3i/v0H7N97KIu5nImMTH0fR0VG4+KSLpdrMSJTsmu1Wu7fe4ijkz7XN9/1Zdrk343KlyhZnJiYOObMn8r+wM38Nmcidna2WcqVXjHnomiiUj/k3o66QzHnrD8JtYT072Oz52qac/L5sXRyKpSp7Ts42OPt3Zx9+7J2LNMrVMyRuKgYw3ScJo5CxTJufDfq9AFn9580TFvnsWHUlqmM/GcyNbws1/gFUBUugvZO6jBT7d27qIqYHs+8jRvhtHQxBceNRVVUvzz56lV9YzxPHoSDAzY1aqAqWtRi2ayKFCY5zYdc7Z27qIua1ptds4a4rFtI4WmjUBczzZ6vZRMe7czcE8lcSQ5reSstBnoCCCFUwKdAJFAHGKEoSiVzKwkh+gohgoUQwYu3vfzCo6CY24bR9I4Tl2lbtwL+479gTj8fRq70R6dLXe9seDR5bawp4/pqPQLp9wf6Bs/Ly2Ru3fS+7NuD74eOoVRpT74fOpZFCywzRvJNelvqzMyuMplTyXDdggUd8GnjRbny9SlRshb58tnyWZePM5Ele+qscZP21KnbijY+3ejfvxcNGtQFYMGCFZSv8B61anuhib7DL9NGvTRjduYEyJfPjvXrFjF4yGgePHhoUjbbsqHww0+DmD93GY8eGfeuhl2+yuyZi/j736Ws37SY82cvok1OzhXZHAra82HrD6hVtRlVyjXAzs6Ojp1fPoTKeJ+m8zJ7DgwfMYg/5iw1yWVlpaa6R2WW/vkXTRq04/GjJ3w7+Mss5TINaianmfvDm5C5Y2m63suuYwBqtZqVK+Ywd+5Srl+/+coZMwqRUYb67RtRslppti/cbJg35L0vGdf2BxYMmsVnoz6nyDvFzK77atnMzEuXLfHwYe52+pTYz3vzNPgEDj/9CMCzoGCeHj2G07y5FBz1M0nnz4NWa8FsZt9sRp4EHCWyTTc0nfuSeOwkhccNM1quLuyIdZlSPDli+j05KWf9pxvniqKEA7FCiBqAF3AKiAWOK4py/QXrLVQUpbaiKLV7t375Y6JiBQeBBxgAACAASURBVPMTnZB6k76d8NBk2Mo/R0PxqqEf71e9lAtPk7UkPHpiWL7jZBitamV9/OpzkREairu7Gqbd3VxMhimkLaNWq3FwsCcuLp7ISDPrRr14iEOP7h355x/9F/c2bvQ1DJV4m7wtdRYRqcG9eGovqJubM5qoaNMyKT2larUaB3t74uISiIxInQ/g5u5MlCaaD5o1IDz8FjExcSQnJ/Pvv9upV7/WS7Pot5f6ut3cXEyG52RUZ/qMxus+r7Pn9X73biz/bt5uqJs7d2LQ6XQoisLixaupnck6y65ja2VlxYZ1i1iz5h/+TRm/mVVRUdG4uqf2YLq6OhOtuWNSxi3N8bS3L0B8XAI1a1dn9LihnDy7ly/79+TbIf3o3bcbAKtXbqRZo4/w+bAr8fH3uHr1Rq7I1rjJe9y4EUFsbDzJycls9fXHs27WhjNFRUXjluZpkaubs8nQmKjIaNxSsqvVauwd8hMfl0Ct2tUZM34Yp8/to99Xvfju+3706duNqMhooiKjOREcAsDmzTuo5lE5S7nSu625Y/SkoZhrUe5Ex7xgjeyT/n1s9lyNjDack8+PZVyc6ZC89ObNm8qVK9f5fc7i184ZHx2Lo2thw7SjiyMJd+JMylV6vxr/x959R0VxPWwc/84uoGLvUuwldrFrxIIFGwg2jEajRmPU2MUWu7EldozG9rMbewvNAqKAFaSIgIoFlWZBwMRYYJn3j8WVpUgRAvrezzmcw87cmXm4M3N39u6dwWJcX9aNXEbCuw8fPGOfxgDw7PETbl0JpHK9qqmWza7EZ89QlvvQ26wsW5bE59r7U375EuLjAXjt4IBurQ/ffr/as5foESOJmWoLSCSEheVYtoSnz9Cp8KEnXlmuLKpn0dr54z5k++e4E3q1tb+Z1+/Snn/dLkJCDn5o+K+JnvPP1jZgGDAc2J407VVObqBepfI8ehZLeHQc8QkqTvvcoX0D7QbCoGQRrt5Rn5j3o17wLl5FySLqr1ATE2XO+obQLZtDWgC8vP2oUaMqVapURFdXFxsbK+wdtG84snc4w5Ah/QHo27cnbucvaqbb2Fihp6dHlSoVqVGjKte8Pv51fUTkE9q3U4/p7GhmSsjddD/r5FufS515e/unyungcFarjIPD2Q85+/TkfFJOB4ezqXJ6efnx6HEELVs2plChggCYmZlyK8U44bSkrLMBNlY4pKgzh3TqzMHhDAPSqDN9/UIUKaL+MKuvX4gundsTGHgbgArJ3nysrbprpmc1Z07t261bVhF86y5r123JVI60+F4PoFq1KlSqbIyuri69+/bklJP2k31OOZ3jm4Hqp1b0su6GR9I4X8tug2jSoCNNGnRk8x+7WLtyE//bsheAMmVKAeqnklj0MufYEYd8kS0sLIJmzU00x1q79q25c1v7npyM+FwPoFr1D7n69O3JKUftXM5OrnwzSP3tj5V1NzwuXAGgZ9dBmNQ3w6S+GZs27mTNqk1s27KXp0+fEx4eSY2a6ra6ffvWqcbKZ9VN32AqVauIUSUDdHV16GHdBbfTGQ8Xyw3qdqPKh3Ogf6+0243B/QDok6zd+JgFC6ZRvFhRptouyJGcD/zvUq6KAWWMy6HU1aGFpSm+KZ54VqleVYYu/RG7kcv5O/qlZrp+scLo6Kn/XUuRkkWp2bQ2ESE5dwEcf+s2SmNjlAYVQEeHgp068vbiJa0yitKlNL8XaPM1CQ+TvklQKJCKFQNAp1o1dKpX551XzvVQvwu8jU5FI3QM1dkKd+3A6wva2ZRlPmQr1L418aHa33IU7taRV5m8yV74b/1/+CdEx4FFgC4wCGib0xvQUSqY2a89Yzb+RWJiIlat6lLDoDQbHa9Qt1I5OjSoxhTrtiw6cI59br4gSSz8trPma8fr98IpX6IIxmUyN542LSqViomT5uDk+CdKhYKduw4SFHSHBfNt8b7uj4PDWbbvOMCunXbcCvIkJiaWQYPVj9YKCrrDkSP2BPi7kaBSMWHibBKTPjHu3bOB9u1aU6ZMKULve7Nw0Up27DzA6NHTWL16ETo6Orx984YxY6Z/LF62TJu/HC/fG8TGvqST9WDGjhhC3xQ39X2Kz6XOVCoVkybNxdFhHwqlgl07DxIUfIf582y57qPOuWPHAXbuWEdQkCcxL2IZPCQpZ7A6p7//OVQJKiZOnENiYiJeXr4cO+bEtaunSEhIwM8vkG3b9mW6zhxT1Nn8+bZcT1ZnO3faEZxUZ98mq7PDR+y5kaLOypcvy5HD6h44pY6SAwdOcObMeQCWL5tDo0Z1kWWZ0IdhjB07I8/2bZuvmzNkcD9uBATh7aW+0J87dznOWXxzU6lUzJy2iMPH/4dCqeTPPUe4fesuM2dPwM/nJqecz7Fv92E2blnBNb+zxMbE8cPwyRmud8fe3ylVqgTx8QlMn7qQuNiXGS7zX2Tz8b6B/cnTnPM4QUJCAgE3gtm940CWc023XciRE9tRKpTs23OEW7fuMmv2RHx9AzjldI69uw+zaetKvP1ciImJZWQm6myG7S9s3rYKPT1dQkMfM27Mpz2KT6VSsWTWSrYcsEOhVHB8vz33bj9g3PRRBPoH43bag/omdVi34zeKlShKB/O2/DTtB6zaDwRg98nNVK1RGf3ChXD1tWfe5MVcPH8121kmTZqLg/1elEolO3cdJDj4DvPmTcXn+g0cHM+yY+cBdmxfS1CgBy9exDLkuw+PHL19+xLFihZFT08XS8uu9LT4lr///ptZMydw61YIV6+ovzn6Y9NOdmRxfyaXqEpk37xtTN09F4VSgcehc0SEPMZ68jeEBtzFz8Ubm1nfUUC/IGM3TgUgOvw5dj8sx7CGMUOX/kiiLKOQJBz/OE7E3Zy7OEel4uXadZRcuQIUCl47OZMQGkqR74cTf/s2by9eQr9vXwq0+RpUKhJf/k3csqQnxujoUPp3O/Xf+Opf4hYvydlhLapEXvy6nnIbloNCwT9/nSL+/kOKjx7Ku6A7vHa/TNFvelOofWt1tri/eT7/N83iSoPyKMuX5e31GzmXKS9kYhjW50jKzPiyz50kSZuAWFmWZ0qS1AGwlWXZIjPLvj79e76soKKW+fcGzNcRHnkdIV2FDHP8s1mOUKQ1fjCfyM9tRP5NBiUKpn4ik/Bxifn4WDPQL5VxoTxwNy4i40J5ZFCFnL1BMyctqxSdcaE88Paf/N1nWtnHJV+9Wb0+uDBXG41CA+bnyd+bv4+CHJB0I2groD+ALMvngfN5GEkQBEEQBEH4VHk4Ljw3fdFjziVJqgvcBVxlWQ7JqLwgCIIgCIIg5KUvuudcluUgoFpe5xAEQRAEQRBymOg5FwRBEARBEAQhN33RPeeCIAiCIAjCF0oWPeeCIAiCIAiCIOQi0XMuCIIgCIIgfH7EmHNBEARBEARBEHKTuDgXBEEQBEEQPj+ynLs/mSBJUjdJkm5LknRXkqRU/2ZYkqRKkiS5SZLkK0nSDUmSemS0TnFxLgiCIAiCIAhZJEmSEtgAdAfqAgOT/sdOcnOAQ7IsNwa+ATZmtF4x5lwQBEEQBEH4/OT9mPMWwF1Zlu8DSJJ0ALACgpKVkYFiSb8XByIyWqm4OM/Aze9d8zpCmv7e+2NeR0hXIcO2eR0hXa8jPPI6QpqKGnfI6wjpSiTPG7/0ZfJrx7yQmE+zdSqVslMn/9BByusI6Uogf+7PWgXK5XWEdPm8iczrCOla+ahyXkdI07v83N4Cdnkd4D8mSdIoYFSySVtkWd6S7LUR8DjZ6zCgZYrVLADOSJI0HigMdM5ou+LiXBAEQRAEQfj85HLPedKF+JaPFEmrRyHlJ/mBwE5ZlldJktQa2CNJUn1ZTv8h7eLiXBAEQRAEQfj85P0/IQoDKiZ7bUzqYSsjgG4AsixfliSpIFAGeJreSsUNoYIgCIIgCIKQdV5ATUmSqkqSpIf6hs+/UpR5BHQCkCSpDlAQePaxlYqec0EQBEEQBOGzIyfm7b0gsiwnSJI0DjgNKIHtsiwHSpK0CPCWZfkvYCqwVZKkyaiHvAyT5Y/flCQuzgVBEARBEAQhG2RZdgKcUkybl+z3IKBNVtYpLs4FQRAEQRCEz0/eP0oxV4gx54IgCIIgCIKQT4iec0EQBEEQBOHzk/dPa8kVoudcEARBEARBEPIJ0XMuCIIgCIIgfH7y+GktuUX0nAuCIAiCIAhCPiEuznNBsQ6Nqe/+Ow08N1Lhpz6p5pe2McPkxk7qnVlNvTOrKTOwc67muRgSgdXav7Bcc5Lt7oGp5kfGvmLkdhcGbHCi/++OeNwJByAg7Dk2G5zUP787ci7ocba239W8A4E33bkV5Mn0aT+lmq+np8ef+/7gVpAnlzztqVzZWDNvxvRx3AryJPCmO+Zd2mumb92yiogwf/x8XbXW1ahRPS562OPtdYYrl51o3swkW5kzMmfpatr1/AbrwaNzZf0pdenSnhs33AgMdMfWdmyq+Xp6euzZs4HAQHfc3U9q6rBUqRKcPn2A58+DWbNmkdYy/fpZ4uV1Gh8fF5Ys+TnTWczNO3Az4AJBQZ5Ms017f+7bu5GgIE88PbT35/RpPxEU5MnNgAt0SbY/x40bga+PC36+rowfP0Izfe6cKTy4743XtdN4XTtNt24dM852053gIE+mpXOs7dv3B8FBnlxMcaxNnz6O4CBPbt5018oWcucKvj4ummPqvYYN6+Lh/he+Pi4cP76TokWLZFBzH3Tq3JarPqfx9nNh4pRRaeb83861ePu5cPbcESpWMtKab2RswKNIP8ZNGKE1XaFQcN7zJPsPf+y/TWeeSfvGrDu3kfUXNmE9pm+q+RYje7HG5XdWnlrHvD8XUcaoLABV6lZlyfFfWX12PStPreNrC9McyfNeo/aNWXVuA2su/EGvManb2B4je7HCZT2/nlrL7GS5yhiVZYnDKpY5rWHFWTs6f9s1R3NB/q0zgMbtm/C72x9sdN9Mn7H9Us3vNdIKO9cNrDltx8L9iymblA1g7u4F7A3Yz+wd81It96m+NmvJSc/92F8+xPfjhqSa36SVCQfO7OB6mDudLcw007+qV5PdDls4dmEvh8/tpqtVpxzP9lX7Rkx3XcXM82swG9Mr1fx2I3ow7ewKpjj/yo/7ZlPSqIxmXs+ZA7E9/Ru2p3+jkUWrHM9Wp30jZruuYe75dXQeY5VqvtmInvx8dhUznH/jp31ztLL1mvkts86s5GeX1fSdPyzHs/1nEhNz9yePfNEX55IklZAkaWyy1x0kSXLI1Y0qFFReMoqQwb9w02wCpa1NKVjTOFWxF39dJNB8CoHmU3i+3yXX4qgSE1lm78WG78w4Nt6CUzdCufc0TqvM1gs3Ma9fiYM/9WC5jSlL7b0AqFGuBH+O7sahn3qwYWhHfvnrKgmqrB2sCoUCu3VLsLAcTINGZgwYYE2dOjW1ynw/fCAxMXHUrmvKWrutLFs6G4A6dWpiY2NFQ5OO9LT4lvV2S1Eo1Ifs7t2H6GnxbartLV86m18Wr6ZZc3MWLlzJ8mWzs5Q3s6x7dGHT6sW5su6UFAoF69YtxspqKCYmnbCx6UXt2tp1OGzYAGJj46hXrx3r129j8eJZALx585aFC1cxc+YSrfKlSpVg2bKf6d59IE2adKZ8+TKYmWX8GNb3WSx7DaFRIzMGDLCiToosw4d/Q0xsHHXrmmJnt5WlSRf+dWqr96eJSUcsLAdjZ7cEhUJBvbpfMeL7gXzdxoKmzczp0aMzNWpU1azPbv1WmrfoSvMWXTl16txHs9mtW4Kl5WAaNjLjm3SOtdiYOOrUNWWd3VaWJjvWBthY0cikIxYpjjWAzl3606y5Oa1a99BM27xpBT/PXkrjJp05ecKZqVPHZFh/73P+tmoBNn1G0rp5d/r2s+Crr2polRn8XT9iY1/SzKQzf2zYwYJF07TmL10+G9ez7qnWPXrsUO7cvpepHJnJOeKXH1kydCGTO4+jTa+2GNesqFXmQeADZlhMwbbbRK44XWLIrGEAvH39lvWT1zKly3iWfLeQYfNHoF+scI7kkhQKhv/yI78OXYRt5/F83astRina2NDA+8y2mMqMbpO46nSJQbOGAhDzNIb5fWYwq8dk5lhNp9eYvpQsVzJHckH+rbP32UYtHs0vQxcwodNPmPZqlyrb/cD72PacwuSuE7jkeJHvfh6umXdi8zHWTl6dY3mS5/p5mS1jB02ld7tBdOvdmWq1qmiViQqPYu7ExTgfP6s1/c3rN8wZv4g+7QczduAUpi2aSNFimf+QnBFJIdF70XC2DfuVFV1sadzra8rX0P6gHB4UylrL2azuPoMbzlfpOWsQAHXMGmNUryqre8zEznouHUZZUqBIoRzN1n/R92watoylXabQtFcbKqTIFhYUygrLWfzafTr+zlexmqV+z6zapBbVmn3F8m7TWGY+lUqNqlOjVd0cyyZ8ui/64hwoAaTuZsxFhRvX5G1oJG8fPUGOT+DFSU9Kdm3xX0bQcjMsmoqli2Jcqii6Okq6NqjM+WDtHnAJePUmHoB/3ryjbFF1A1JITwcdpfoQeZegQkLK8vZbNG/MvXuhPHjwiPj4eA4dOkkvS+3eql6W5uzZcxiAo0cd6WhmmjS9K4cOneTdu3eEhj7m3r1QWjRvDICH51VexMSm2p4syxQtVhSAYsWLEhH5JMuZM6OZSQOKJ20ntzVvbqJVh4cP22Npaa5VxtLSnL17jwBw7JiT5kL7339fc+mSF2/fvtEqX7VqJUJCHvD8+QsAzp3zxNq6e5azHDp0Ms0smv15zBGzpP1paWmean82b25C7do1uHrVl9ev36BSqfBwv4KVVbcs11PKY+3goZNYpjjWLNM51iwtu3IwnWMtPbVqVcfD4woALq4e9O7d46Pl32varCEP7j/kYehj4uPjOXbUke4W2j1+PXp25sCfxwA4eeIU7Tq0/jDPojOhoY+5FRyitYyhYQW6dO3Anl2HMpUjIzVMahIVGsXTx09IiE/gor0Hzbpot2WBlwN49+YdAHd8b1PKoDQAkQ8iiAqNBCDm6QvinsdRrFSxHMwVydPHT1DFJ3DZ3pNmXVpqlQm6fFOT626yXKr4BBLeJQCgq6eLpMh6m5ZxtvxXZwA1TWoSGRrJk0fqbJ727rQw1663m5cDePfmrSZb6aRsAAEXb/D6n9c5lue9+o3r8vhBGOGPIkiIT+DUCRc6dG2rVSbicRQhwfdITNGT+fD+Yx49CAPg2ZPnvHgeQ8nSJXIsWyWTGkQ/jOLF46eo4lX42V+mnnkzrTL3LgcRn7Q/H/repXiFUgCUr2nEvavBJKoSeff6LRHBD6ndvlGOZatsUoNnD58QnZTNx/4SDcyba5UJuRyoyRbqG0KJCur9KSOjW0AXHV0ddPR0Ueoo+ftZXKptfBZEz3nekCSpiiRJtyRJ2iZJ0k1JkvZJktRZkqSLkiSFSJLUQpKkBZIkbZck6bwkSfclSZqQtPhyoLokSX6SJK1ImlZEkqQjSevcJ0lSjrbOehVK8S7iueb1u8hodCuUTlWuZI9W1Du7hupbpqFnmHp+Tnn68jUViutrXpcvrs/Tv7Ub2NEdG+Lo/wDzFccYt+c8M3t+aHwCHj+nj50D/X53ZE6vFpqL9cwyNKrA47AIzeuw8EgMDSukW0alUhEX95LSpUtiaJjGskbay6Y0xXY+vy6bw4N7Xvy2fC6z5yzLUt78yNCwAmHJ6iE8PBJDw/LpllGpVLx8+TelS6ffI3jv3kNq1apO5crGKJVKLC3NMTY2zDCLkaEBYY8jk2WJwtDIIEWZCoSFRWqyxL1M2p9GBprpAOFhURgZGhAYdJu2bVtSqlQJChUqSLduHbWyjBk9jOveZ9myeSUlShRPN5uhUep6MsrksWaUVh0nHWuyLOPstJ+rV5wZOeLDtzWBgbc1H0z69bWgYibqD8DAoALh4R/qISI8CgMD7f1pYFie8LAoTc6Xcf9QqnRJ9PULMXHyKH5btj7Vepf+OpsFc39LdQGTXaUqlCY68kNb9iIymtJptGXvdRrQBd/z11NNr9GoJjp6Ojx5GJUjuUpWKKWVKzoympJJF0Rp6TCgM/7nfTSvSxmU4ddTa/n9yjb+2nSMmKcxOZIL8m+dvc/2PEK73kqXTz9b5wFd8HFLnS2nlTMoS1TEh06Up5HPKG9Q9iNLpK1+4zro6uryODQ8x7IVL1+S2IhozevYyGiKl0+/XW1p04Fb5/0B1BfjHRqhW1AP/ZJFqdG6LiUMcu69vkT5UlnK1srGjKDzfgCE+oRw53Igv3htZvG1zQS7+/PkXs7Vm/Dp8v3FeZIawDqgIVAbGASYArbA+8GytYGuQAtgviRJusBM4J4syyayLL//XrgxMAmoC1Qji/9SNUNpXevL2ncTx5715karHwnsMpmXHjeounZijkbQ2jSp72ROmfDUjVB6NanOmWl9+H1IB+YcvURi0h3QDSqW4dgEC/b92I3/uQfyNl6Vpe2n9dlHTlEfaZfJ3LIp/TjqO6ZOW0DV6s2ZOm0hWzevylLe/Cj7dZh+XcXGxjFhwmz27NmAq+sRHj4MIyEhIRNZUk/LbJb0lr116y4rVm7E2Wk/DvZ7uREQpMmyectuatdpQ7Pm5kRFPeW3X+d+JFvuHGvtO1jTomU3LCwHM2bMMExN1b2NP4yawpjRw7h6xZkiRQvz7l18utm0M6Seltk6nDl7An/8voNXr/7VmmfezYxnz6Lx90t9T0lOSu+Yatu7PdUa1OCvzce1ppcoV5Lxayaz0dYuw3M3s9L8Bi+dVZsm5bJPlutF5HNmdJvE5HajadfXjOJl0v/AlxPyQ51B1tqI9r07UL1hDU5sPpZj209PZs6HjJQpV5ol6+cxb9KSHK2ztMKlt/om1qYYN6zG+S32ANzxCOCWmx/jji1ksN14HvqEoFJl7f0zp7I1szalUsPqnNvyFwBlKpenQg0j5rUaw9xWo6n1dX2qt6iTc9n+S7Kcuz955HO5OH8gy3KALMuJQCDgKqvPwACgSlIZR1mW38qy/Bx4CpRPe1Vck2U5LGldfsmW15AkaZQkSd6SJHkffxWapaDvIqPRM/xw04WeQWnin7zQKqOK+Rs56avVZ/vOot+gWpa2kRXli+kTFffhjfxJ3L+aYSvvHb9+D/P6lQBoVKksbxMSif33rVaZauWKU0hPh7tPUw8l+ZjwsEitHkVjIwMiUww1SV5GqVRSvHgxXryIITw8jWUjPj5M5bsh/Tl+XH3T3pEj9jRvnjs3hP6XwsMjtXqSjYwMiIx8mm4ZpVJJsWJFefHi4/vKycmFdu2s6NChNyEh97l7NzTDLGHhkRhX/NBTbmRUgciIqNRljA00WYoXK8aLF7GEh32YDmBkXIGISPWyO3ceoGWr7nTq3I+YF7HcvfsAgKdPn5OYmIgsy/xv+58f3Z/q9WvXU8phTekda2Fp1XHSsfb+eH32LJoTJ501GW7fvkePnoNo2ao7Bw+e5P79jOsPICIiCqNk3zYYGlUgKkp7f0aER2FkXEGTs1jxIsS8iKVps0Ys+GU6fjfdGD12GJOnjmbkqMG0bNWE7j064XfTjW0719K2XSs2bV2ZqTzpeREVTWmDD21ZKYPSvEjRlgE0aNOIPuP68+vIJZohIwCFihRi1o657F+5lxDfO5+U5WO5ShuUJiaNXPXbNMR6XD9Wjlyqleu9mKcxhN15zFctcm6sbX6tM4DoyOeUMdSutxdPU2draNqIfuNsWDZicZr1ltOeRDyjQrJvAssZlOVp1POPLKGtcBF9ft+7kt9/3UKAT85+OI2LekGJZN9slzAozcs0vmmp2aY+ncZZs2PkSlTJ6sx1wwnW9JjFliFLQZJ4/iDnvgmJjYrOVLZabRpgPq4PW0b+ptmfDbu2INQ3hHf/vuXdv28JPu9HlcY1Uy0r5J3P5eI8+ZViYrLXiXx4VnvyMirSf4Z7huVkWd4iy3IzWZab9S5cJUtBX/mFUKCqAXoVyyHp6lDKypSYM15aZXST3YBUwrw5b+6GZWkbWVHPqDSPov8mPOYf4hNUnA54SPva2jdPGZTQ5+o9daNx/2kc7xJUlCxcgPCYfzQ3gEbE/sPD5y8xLJG1G5S8vP2oUaMqVapURFdXFxsbK+wdzmiVsXc4w5Ah/QHo27cnbucvaqbb2Fihp6dHlSoVqVGjKte8fD+6vYjIJ7Rvpx6f29HMlJCki7zPmbe3v1Yd9u9viYOD9o1RDg5nGTxY/fSFPn16cP78pQzXW7asumEvUaI4o0YNYceO/VnOYmNjlWYWzf7s05PzSfvTweFsqv3p5eWnlaViRUOsrdUXuwAVKpTTrNfKqhuBgbfTzZbyWBtgY4VDimPNIZ1jzcHhDAPSONb09QtRpIj6mNfXL0SXzu01Gd5nliSJn2dNZMuWPRnWH4DP9QCqVa9CpcrG6Orq0qdvT045aj91yNnJlW8GqZ9CYmXdDY8L6rHtPbsOwqS+GSb1zdi0cSdrVm1i25a9/LJgFfVrt8Wkvhkjh03Cw/0Ko3+wzVSe9Nz1D8GgqgHlKpZDR1eHNpZt8T57TatMlXpVGbVsDL+OWMLL6A9jVnV0dZi2ZRYXjrpxxSnjYzEr7vmHUKGqAWUrlkOpq0NrS1Oup5Fr5LKxrByxVCtXqQql0S2gB0DhYoX5qlltIu9FkFPya50BhPiHYFDVkHIVy6Ojq4OpZTu8UmSrWq8aY5b9xNIRvxAX/d+MQQ70C6ZSNWOMKhmgo6tDN+vOXDjjmalldXR1WLNjOfaHnTlr75bj2R7736NMlQqUMi6LUleJiWVrAs9qD/UxrFeFvktHsmPkSv6JfqmZLikk9Euob041qF0Jw9qVuONxI8eyPfK/R9lk2ZpYfk3AWW+tMsb1qvDN0pFsHfmbVraYiOfUaFkXhVKBQkdJ9ZZ1eJKL1yG56gsdc/6l/xOiv4H/5q6991SJPJqzla/+x0XAvwAAIABJREFUnA8KBc8PuvLmzmMMbQfyr/9dYs96Uf77npQwb46sUpEQ+w8PJqUeP5pTdJQKZlo0Y8yucyQmylg1qU6N8iXY6OpPXcPSdKhjzJRuTVl08gr7Lt0CSWJhn9ZIkoTvw6dsdw9CR6lAIcEsi+aULFwwS9tXqVRMnDQHJ8c/USoU7Nx1kKCgOyyYb4v3dX8cHM6yfccBdu2041aQJzExsQwarL6HNyjoDkeO2BPg70aCSsWEibM142n37tlA+3atKVOmFKH3vVm4aCU7dh5g9OhprF69CB0dHd6+ecOYMdNzvE4Bps1fjpfvDWJjX9LJejBjRwyhr2XOP5YN1HU4adJc7O33oFQq2bXrIMHBd5g3bwrXrwfg6HiWnTsPsn37WgID3XnxIpbvvhunWf727YsULVoUPT1dLC27YmExmFu3Qli1agENGqh7DZcuXavprc5MFkeHfSiUCnbtPEhQ8B3mz7Pluo96f+7YcYCdO9YRFORJzItYBg9J2p/B6v3p738OVYKKiRPnaPbnwQNbKF26JPHxCUyYOJvYWPWFwbKls2nUqB6yLPPw4WPG/jTzo9kmTpqDY4pjbf58W64nO9Z27rQjOOlY+zbZsXb4iD03Uhxr5cuX5cjh/wGg1FFy4MAJzpw5D8A3A6wZPWYYACdOOLFz18FM78/ptgs5cmI7SoWSfXuOcOvWXWbNnoivbwCnnM6xd/dhNm1dibefCzExsYwcPjlT685JiapE/jdvC7N3L0ChVOB2yJWwkMcMmDKIezfu4u1yjSE/D6egfiGmblSfZ88jnvPryCW0tmhDnRb1KFqiKGb91I+/3GBrR2jQp39YTlQlsnPeVmbtno9CqeT8IRfCQh7Tb8pAHty4y3UXLwb9PIyC+gWZmJQrOuIZK0cuxaiGMYPnDE8aZiXhsOUkj28//ORMybPlxzp7n23r3E3M37MQhVKB60EXHt95xMAp33I3IASvs9cYOns4BfULMu0P9Xn2LOIZy0aon0q15MhyjKobU7BwQbZe3cGGaXb4uX+8syQzVCoVy35ezR/716BQKjmx34F7tx8wdvpIAv1uceGMJ/VM6rBm+zKKlShK+y6mjJ02gj7tB9O1VyeatDKheMli9BqgviF73sQl3A4MyWCrmZOoSuT4vJ38sHsWklKB16HzPAkJo+vkfjwOeECQy3UsZg2igH5BhmxUD0+NDY9mxw8rUerq8NPh+QC8+ec1f07eQGIWn3aWUbYj87YzdvfPKJQKrhw6T1RIGD0m9+dRwH1uulzHatZg9PQLMnyjuv2ICX/O1h9W4Od0hVpf12fm6ZUgywRf8OOmq08GWxT+S1KOjs/KBZIkVQEcZFmun/R6Z9LrI+/nAUeAf2RZXplU5iZgIctyqCRJf6Ieq+4MOAK2sixbJJX7HfCWZXlnetv3MuqdLyuo/pqPP0kiLxUdvDmvI6TrdYRHXkdIU1HjDnkdIV2Jct71HmQkP7dfRQvoZ1woD3QqlX8fmaaTjSdC/VcS0hvYnscS8vH5ef9ddMaF8kiXgpXzOkKa3pF/9yeAXejBfHWS/rtyZK6emPq22/Lk7833PeeyLIcC9ZO9HpbevGTTk5cflGL2+WTzxiEIgiAIgiAI+US+vzgXBEEQBEEQhFTy8TdHn0JcnAuCIAiCIAifn8T8OdzsU30uT2sRBEEQBEEQhC+e6DkXBEEQBEEQPjtyHj7uMDeJnnNBEARBEARByCdEz7kgCIIgCILw+RFjzgVBEARBEARByE2i51wQBEEQBEH4/Hyhj1IUPeeCIAiCIAiCkE+InnNBEARBEATh8yPGnAuCIAiCIAiCkJskWf4yP3XklIIFK+XLCkr8QsdZ5TaFlD8/j/4ddj6vI6SrQd0BeR0hXV/rV87rCOk68swnryOkKTEft/kKScrrCOl6p0rI6whpUiWq8jpCukoUKpLXEdJVpkDxvI6Qppfxr/I6wkeFxwTmq5P01YKBudqgFV6wP0/+3vx5pSIIgiAIgiAI/w+JMeeCIAiCIAjC50eMORcEQRAEQRAEITeJnnNBEARBEATh8/OF3n8nes4FQRAEQRAEIZ8QPeeCIAiCIAjC50eMORcEQRAEQRAEITeJnnNBEARBEAThsyMnijHngiAIgiAIgiDkItFzLgiCIAiCIHx+vtAx5+LiXBAEQRAEQfj8fKEX52JYyyfo0qU9N264ERjojq3t2FTz9fT02LNnA4GB7ri7n6RyZWMASpUqwenTB3j+PJg1axZpLaOrq8uGDcsJCDiPv/85rK27ZyubuXkHbgZcICjIk2m2P6WZbd/ejQQFeeLpYa+V7czpQ7yIvs3atYu1llm0cDr37l7jRfTtbGX6lFwA06f9RFCQJzcDLtClS3vN9AkTRuLn64qvjwt7dv9OgQIFspUtN/Znv36WeHmdxsfHhSVLfs5WrqyYs3Q17Xp+g/Xg0bm+rYyYmrXG+dIRTl89xg/jh6aa36xVY4667OFmxGW6WnTM1Sz125uw1HUdy86vp8cY61TzzUdYsPjsGhY6r8J233xKG5XRzNt27yALnFawwGkF47fOyJE8nbu047qvC343zjF5aup9paenx45ddvjdOMe588eoVMkIgKZNG+J52QHPyw5cvOKIhaW5ZpmAIHcuX3PG87ID5z1OZjuXj58r/gFuTEkn167d6/EPcMPtwnFNLrOOpnhc/Iur15zxuPgX7du31izTt29Prlx1xsv7NL8snpmtXO+z5WSdFSigh9uF41y84shVr1P8PHtStrPlp/cCc/MO3LzpTnCQJ9OmpdPG7vuD4CBPLnqmaGOnjyM4yJObN9212tjixYtx4MAWAgIucOPGeVq1bArAggXT8Ll+Fm+vMzg5/omBQflMZQTo2Kktl71Pcc33DBMm/5BGTl227ljDNd8znHI9RMWk/VmxkhGPovxx8ziBm8cJVqxZmGrZPfv/wP2yfaazfIypWSucLh3m1NWjjBz/Xar56nZsNwERlzBP0Y5tObCOqyGu/LF3dY5kAejQyRT3aw54Xnfmp0kjU83X09Plj/+txPO6M/Zn92Nc0VAzr069Wvx1eh/nLp3E5eJxChTQA8Cqbw9cLh7nrOcx9h7eTMlSJXIsr5A9eXZxLklSFUmSbmah/E5Jkvol/b5NkqS6aZQZJknS7zmZMz0KhYJ16xZjZTUUE5NO2Nj0onbtmlplhg0bQGxsHPXqtWP9+m0sXjwLgDdv3rJw4SpmzlySar0zZ47n2bPnNGjQAROTTnh4XMl2NsteQ2jUyIwBA6yokyLb8OHfEBMbR926ptjZbWVp0oXjmzdvWbBwBTNm/pJqvQ6OLrQxtchynpzIVad2TWxsrDAx6YiF5WDs7JagUCgwNKzATz99T6vWPWncpDNKpRIbm17ZzpaT+7NUqRIsW/Yz3bsPpEmTzpQvXwYzszZZzpYV1j26sGn14owL5jKFQsG8X6fzw8CJWJja0LOPOdVrVdUqExkexawJC3E4djpXs0gKBYMXjWTNsCXM6TKZlr1MMaxhrFXmUdADFlnOYH73qXg7X6b/rCGaee/evGNBj2ks6DGN9T/8+sl5FAoFq1YvpG/v4TRv2pV+/S35qnYNrTLfDbUhNvYlJg07suH37Sz8Rf2hICjoDu1NrTBtbUEf62GsW78YpVKpWa5n90GYtragQ1urbOVavWYRfayH0ayJOf3796J2ilxDh9kQGxtHowZmbFj/P83FdnT0C/r3G0nLFt358Qdbtv5PfTFSqlQJFi+dhUXPb2nerCvlypWhQ4evs5Utp+vs7dt3WPT4ljatetKmtQWdu7SjeXOTbGXLL+8FCoUCu3VLsLQcTMNGZnwzwJo6dbSzfD98ILExcdSpa8o6u60sXTobgDp1ajLAxopGJh2xsPiW9XZLUSjUlwhrVi/izGk3GjRoT9OmXQi+FQLAqlV/0KRpF5o1N8fJyYU5sydnus6Wr5rHN/1G0qZFT3r3taDWV9W1ynz7XX9iY1/SorE5mzbuZN5CW8280AePMGtrjVlba6ZNnq+1XE/LLrx69SpTOTKTc+6v0xk1cCKWpgPo2adrqnYsIjyKWRMW4XjsTKrlt2/Yy4yf5qea/il5lqyYzeD+ozFr1Qvrvj2omaLeBg7pS1zcS0ybdmfrH7uZvWAKAEqlErvNy5k5dREdv7aiv8Uw4uMTUCqVLFo2k/6Ww+li2ofgoDsM/2FQjmXOdXJi7v7kkc+y51yW5ZGyLAflZYbmzU24dy+UBw8eER8fz+HD9lgm68UCsLQ0Z+/eIwAcO+akuTD799/XXLrkxdu3b1Ktd+hQG377bQMAsiwTHR3zydkOHTqZZrY9ew4DcPSYI2ZmplrZ3rx5m2q91675EBX1NMt5ciKXpaU5hw6d5N27d4SGPubevVDNG6mOUodChQqiVCoppF+IyMgnn5wtJ/Zn1aqVCAl5wPPnLwA4d84z29+EZFYzkwYUL1Y0V7eRGQ2b1OPRg8eEPQwnPj4Bp+Nn6dStvVaZ8MeR3Am6i5zLX0tWM6nB04dRPHv8FFV8AlftL2Ji3lyrzK3Lgbx78w6A+74hlKxQOtfyNGvWiPv3HxIa+pj4+HiOHnGgp0UXrTI9LTqzf99RAE4cd9Zc0L5+/QaVSgVAwQIFkHOw6po1a8T9ex9yHTlinzpXzy7s26vOdTxZrhv+QURFqtuGoKA7FChQAD09PapUrcTdZOeAm9tFrKy7ZS9bLtTZq1f/AqCrq4OOrg5yNio0P70XtGjeWCvLwUMnsbTsmiqLpo096khHTRvblYMp2tgWzRtTtGgRTE1bsn3HfgDi4+OJi3sJwN9//6NZr35h/UzXX5OmDQm9/5CHoWHEx8dz4pgj3Xt20irTvUdHDv55HAD7E6dpm+zbmPQULqzPmJ+Gs3rFH5nKkRF1OxZG2MOIpHbsDB27tdMqE5HUjiWm8dSQKx5evPrn3xzJAtC4aQNC7z/m0UN1vZ085kTXHmZaZcy7d+TwfvU3Z44nz2DavhUA7Tt+TXDgHYJuqr/5jomJIzExEUmSkCQJ/cKFAChatDBPop7lWGYhe/L64lwpSdJWSZICJUk6I0lSIUmSTCRJuiJJ0g1Jko5LklQy5UKSJJ2XJKlZ0u/DJUm6I0nSBaBNsjKWkiRdlSTJV5IkF0mSykuSpJAkKUSSpLJJZRSSJN2VJKlMym1kxNCwAmFhEZrX4eGRGBqWT7eMSqXi5cu/KV061Z+jUbx4MQDmz7fl8mVH9u37g3LlshwNI0MDwh5HJssWhaGRQYoyFQgLi9Rki3v58qPZcsKn5DI0MtBMBwgPi8LI0ICIiCjWrN3MvbtXefTQh5dxf+Pi4p7lbLmxP+/de0itWtWpXNkYpVKJpaU5xsaG6Zb/kpSvUJbI8A8fkqIin1DeoGyeZClRvhQvIp5rXsdERlOyfKl0y7e16UjAeV/Na90Cesz761dmH19K4xQX9dlhkOwYB4gIj8QwxXAAA8PyWufBy5d/UyrpWGvWrBFXvU5x+ZozkybM0Vx4yrLMib92ccHzJMOGf5PlXIaGFQgLT3F+GlZIUaa8poz6/Ex9Dlhbd+eGfyDv3r3j/r1Qan1VnUqVjJLOgS4YZeMcyK06UygUeF524F6oF27nLuLt7Z/lbPnpvcDQKHUWo5T70KgCj5NliYtTt7FGaf0dRhWoVq0yz59H879ta/C6dprNm1agr19IU27Rohncv+fFwIG9WbBwRYYZQb2vwsOjNK8jwp+kGhJTwaA84eEp9mcpdZ1VqmzMOY/jnHTcQ6vWTTXLzJw9kY2/b+f169QfdrKjXIWyRCVrx55EPs2zdgzUdRKR7ByNjHhChZT1ZliOiKS6fV9vJUuVoFr1KiDL7DuyhVPnDzNmwvcAJCQkMGvqL7h6nsAn+Dw1v6rO/j1H/7O/6ZMlyrn7k0fy+uK8JrBBluV6QCzQF9gNzJBluSEQAKT7nZAkSQbAQtQX5V2A5ENdPIFWsiw3Bg4A02VZTgT2At8mlekM+Muy/DzZckiSNEqSJG9JkrxVqn9IiyRJqaal7DXITJnkdHSUGBsbcvmyN61b9+Tq1essXz4n3fLpSWOzn5wtJ3xKrvSWLVGiOJYW5tT6qjWVqzSlcOFCDBrYJxvZcn5/xsbGMWHCbPbs2YCr6xEePgwjISEhy9k+S3lwfKUnK/utlXVbqjSszqktH8ZsT/t6NIt6zWDLhLUMnDecspUyP6427Typp6U61kizEADe3v60bN6NDu2smWo7RjNu1LxTf9q16UXf3t/zw49D+LpN1j5I5MQ5UKdOTRYtnsGE8eqhErGxL5k0cS679vzOGZdDPHwYjiob50Bu1VliYiKmrS2oU+trmjZtSJ26tbKRLf+8F2Q/S/rL6iiVNG7cgM2bd9O8RVdevfqX6dPHacrMm/cr1ao3Z//+44wdOzzDjJ+WU+ZJ1FMa1zOjY9vezJ29nE3bVlGkaGHqN6hN1WqVcHJwyVSG7OfMsdVn2aecB0odJc1bNWHcqOlYdx9C956dMG3XEh0dHb77fgBd2/ejSZ0OBAfeYXwa9wAI/628vjh/IMuyX9Lv14HqQAlZli8kTdsFtEtzSbWWwHlZlp/JsvwOOJhsnjFwWpKkAGAaUC9p+nbg/V0d3wM7Uq5UluUtsiw3k2W5mVJZJM0Nh4dHavWCGhkZEBn5NN0ySqWSYsWK8uJFbLp/THR0DK9e/cvJk6cAOHbMEROT+umWT09YeCTGFT/0SBsZVSAyIip1GWMDTbbixYp9NFtO+JRc4WEfpgMYGVcgIjKKTh1NCQ19zPPnL0hISODECWetnpTMyo39CeDk5EK7dlZ06NCbkJD73L0bmuVsn6MnkU8xMPpwEVvBoDxPo55/ZIncExMVTSnDD72OJQ1KE/s09RCBum0aYDGuL3Yjl5Pw7sMF5Puyzx4/5daVQCrVq5pq2ayICI/SOpYNjQyITDFcLCIiSus8SOtYu3P7Hq9e/Uvdul8BaIacPX8WjcNfZ2jarFGWcoWHR2JslOL8TDFELDw8SlNGfX5+yGVoVIE/D2xm1MipPHjwSLOMs5MrZu1708msb7bPgdyqs/fi4v7G0+Mqnbt87O0mbfnpvUDdTmpniUi5D8MiqZgsS/HixXjxIiap7U3xd0Q8ISw8krCwSK55qb9NOnrMkcYmDVJt+8CB4/Tu3SPDjKDen0ZGH3r0DY3KpxoyGRkRhZGR9v6MiYnl3bt4YmLUdXfDL5DQB4+oXqMqzVo0ppFJfa7fcMXh1J9Ur1GFEw67M5UnPU8in1IhWTtW3qAcT/NwyEdkxBOtb5sNDMvzJFW9PcEwqW4/1FsckRFPuHLRm5gXsbx5/YZzZz2o36gu9RrUBuBh6GMA7E+comnLrN97kVfkRDlXf/JKXl+cJx/YrAKyc4twerW3HvhdluUGwI9AQQBZlh8DTyRJ6oj64t45G9vE29ufGjWqUqVKRXR1denf3xIHh7NaZRwczjJ4cD8A+vTpwfnzlzJcr6Oji+ZJB2ZmbQgODvnkbDY2VmlmGzKkPwB9+/Tk/PmLWd7Of5nLweEsNjZW6nGsVSpSo0ZVvLz8ePQ4gpYtG1OoUEEAzMxMuXXr7idny6n9WbaseuxyiRLFGTVqCDuSxm1+6QJ8g6hcrRJGlQzR1dWhR+8unDud9eFGOeGB/13KVzGgjHE5lLo6tLRsg99ZL60ylepV5bulP2I3cjl/R7/UTNcvVhgdPfUTZ4uULErNprWJDAn7pDzXr9+gWvUqVK5sjK6uLn37WeDkqN3b5+ToysBv+wJg3bs7Fy5cBtAMkQKoWNGQmrWq8fBRGPr6hShSpLA6s34hOnYyJTjoTpZzVa/xIVe/fpapczm58O1gda7eyXIVL16Uo0e3s2Deb1y5cl1rmQ/nQDF+GDWYXTsPklW5UWely5SieHH1/RkFCxagg1kbQm7fz3K2/PRe4OXtp5VlgI0VDg7aNyo6OJz50Mb27Ymbpo09w4AUbew1L1+ePHlGWFgEtWqpbzzs2NGU4GD1sVWjxocPqpYW5ty+fS/DjAC+PgFUrV6FSkn707pPT045ndMqc8rpHAMG9Vav27ornu7qG2JLly6puVG1chVjqlWvwsPQx+z8334a1G5L04adsOg2iHt3Q7G2SP10laxQt2MVk7Vj5rid9vikdX4KP5+bVK1eiYqVjNDV1cWqTw/OOLtplTlzyo3+A9U3hPe0Muei+1UALrhepE69WhRMuj+rVZtmhNy+R1TkE2p+VV0zBKxdh6+5m43zQMhZ+e0553FAjCRJbWVZ9gCGABc+Uv4qsE6SpNLAS6A/8H7QYHEgPOn3lM9x24Z6eMseWZZV2QmqUqmYNGku9vZ7UCqV7Np1kODgO8ybN4Xr1wNwdDzLzp0H2b59LYGB7rx4Ect33334KvD27YsULVoUPT1dLC27YmExmFu3QpgzZxnbt69lxYr5PH/+glGjpmY7m6PDPhRKBbt2HiQo+A7z59ly3ccfB4ez7NhxgJ071hEU5EnMi1gGD/nw+K87ty9TrJg6Wy/LrvTsOYjgWyEsWzqbAQOs0dcvxP17XuzYsZ9fFmf+EVGfkiso+A5Hjtjj738OVYKKiRPnkJiYiJeXL8eOOXHt6ikSEhLw8wtk27Z92a6znN6fq1YtoEED9WirpUvXcvfugyxny4pp85fj5XuD2NiXdLIezNgRQ+ib4oaw/4JKpeKXmb/xv4N2KJRKjv75F3dv32f8jB+56ReM22l36pvU5fedv1GseDHMzE0ZN/1HLNsNyPEsiapE9s7bxpTdc1AoFXgeOkdESBjWkwcQGnAPPxdvbGYNoYB+QcZuVJ9v0eHPWf/DrxjUMGbo0lFJQ6sknP44TsTdT7s4V6lUTJu6gOMnd6FUKtiz+zC3gkOYPWcSPj4BODu5snvXQbZsW43fjXPExMQxfOgEAFp/3YzJU0YTn5BAYmIiUybN40V0DFWqVGTfgU0A6CiVHD70Fy5ns/ZhSKVSMXXKfE78tVuTKzg4hDlzJ+PjE4CTowu7dh5k2//W4B/gRkxMHMO+Gw/Aj6OHUq16ZWbMGs+MWeppVpbf8exZNL+tmEeDBnUAWL7MLlvnQG7UWb36tdm0ZQVKpRKFQuL4USdOnTqXQZK0s+WX9wKVSsXESXNwdPwTpULBzl0HCQq6w/z5tly/rm5jt+84wM6ddgQHeRITE8u3g5Pa2KA7HD5izw1/NxJUKiZMnK25yXHS5Lns3rUePT1d7j94xMiR6ieALFkyi1q1qiMnJvLwUTg//ZS5R2WqVCpm2S7i0LFtKJRK9u89yu1bd5nx8wT8fG9y2vkc+/YcYeOWFVzzPUNMTByjvlc/CaZ1m+bM+HkCCQkqEhNV2E6eT2xMXJb2WWapVCoWz1zBtoN2KJQKjv1pn9SOjUpqxzyob1KH9Zp2rC3jp4/Csp36no89f22hWo3K6BcuhJufPXMmL+GiW9afwJY8z5zpS/jz6BYUSgUH9x3nzq172M4ah79fIGed3Tiw5yh2m5bjed2Z2Jg4xo5QP+UmLu4lWzbuwsn1IDIy58564HpG3Uas+W0jxxx3EZ+QQPjjSCaPzf3H/uaYL/Q551IejgOtAjjIslw/6bUtUAQ4AWwC9IH7wHBZlmMkSdqZVP6IJEnnAVtZlr0lSRoOzAIiAT9AKcvyOEmSrIA1qC/QrwDNZVnukLQtXSAaaCHL8q2P5SxYsFK+3POJefiIn8+ZQsrrL4vS9nfY+byOkK4GdXP+gjmnfK1fOa8jpOvIM5+8jpCmxLwcNJsBRVqDavOJd6r8eb+IKjFb/Uv/iRKF0h4Wmh+UKVA8ryOk6WV8zjwGMreExwTmq5P07wkWudqgFbVzyJO/N896zmVZDgXqJ3u9MtnsVmmUH5bs9w7Jft9B2uPGTwLp/SeORqhvBP3ohbkgCIIgCIKQT6XxCMsvQX4b1pLrJEmaCYzhwxNbBEEQBEEQBCFf+H93cS7L8nJgeV7nEARBEARBED7BFzrmPH8OwBUEQRAEQRCE/4f+3/WcC4IgCIIgCF8A0XMuCIIgCIIgCEJuEj3ngiAIgiAIwmcnrx4HnttEz7kgCIIgCIIg5BOi51wQBEEQBEH4/Igx54IgCIIgCIIg5CbRcy4IgiAIgiB8fr7QnnNxcZ6BhERVXkf47Eh5HeAjEsmf/+q3Qd0BeR0hXQFBB/M6QrrKVOmS1xHSlZhPb1R6k/AuryOkKz+3HSUKFcnrCGl6+fbfvI6QLqWUf7+cf/H2ZV5HSJOOQpnXEYR8QFycC4IgCIIgCJ8dWfScC4IgCIIgCEI+8YVenOff75wEQRAEQRAE4f8Z0XMuCIIgCIIgfH7y521kn0z0nAuCIAiCIAhCPiF6zgVBEARBEITPzpd6Q6joORcEQRAEQRCEfEL0nAuCIAiCIAifH9FzLgiCIAiCIAhCbhI954IgCIIgCMLnRzytRRAEQRAEQRCE3CR6zgVBEARBEITPjnhay/9jW7esIiLMHz9f13TLtG/XGm+vM/j7neOcy5Esb2PG9HHcCvIk8KY75l3aa6bfvXMFXx8XvL3OcOWy00fX0dW8A4E33bkV5Mn0aT+lmq+np8ef+/7gVpAnlzztqVzZ+KPbL1CgAJcvOnDd+yz+fueYP2+qpvzYMcO4FeRJwrtwSpcu+dFc5uYduHnTneAgT6alk2vfvj8IDvLkYopc06ePIzjIk5s33emSrF5C0qmXuXOnEPrAG2+vM3h7naFbt44ZZwu4QFCQJ9Ns08m2dyNBQZ54eqTINu0ngoI8uRlwQSvbuHEj8PVxwc/XlfHjR3zINmcKD+5743XtNF7XTmeYLTNMzVrjfOkIp68e44fxQ1PNb9aqMUdd9nAz4jJdLT59e59iztLVtOv5DdaDR/8n2+vUuR3ePmfx9T/H5Ck/ppqvp6fHjl12+Pqfw9XtKJUqGQHcJoYeAAAgAElEQVTQpGlDPC7Z43HJHs/LDlhYmmuWKV68KLv3/o6XzxmuXT9N8xaNs5Wtc5d2+Pi54h/gxpSpqetDT0+PXbvX4x/ghtuF45psTZs14tIVRy5dceTyFScse33ItnHTrzwI9eKa16kMt5/TbcXH1pleWzF1ymjNeern68rb148oWbKEVo6cbjtq1aqu2aa31xmin99iwviRQNbbjuQ6dmrLZe9TXPM9w4TJP6SRU5etO9ZwzfcMp1wPUTFpf1asZMSjKH/cPE7g5nGCFWsWAlCoUEH+PLSZS17OeFxxYO6CqanWmVnmXToQcOM8QYEe2NqOTSObHnv3bCQo0AMP9780dViqVAlOnz5I9PNbrF3zS5rrPnpkOz7XXbKd7T2zTqZ4ejlx2ecU4yaNTCOjLpu3r+ayzymcXA5QsZKhZl6derVwOLOfC5ftcbt4kgIF9HIkz0VvZ674nmZ8Ovtzy47VXPE9jbPrQa39GRrlh6vHcVw9jvPbmgUAFC5SWDPN1eM4Qfcv88uyWdnK1qGTKe7XHPC87sxP6dTVH/9bied1Z+zP7se4onZd/XV6H+cuncTl4nEKFNCjcBF9zrgf1fwE3PVk4dKZ2com5JzPoudckqTzgK0sy94fKTMMaCbL8ric3v7u3YfYuHEHO3asS3N+8eLFWL9+KT0tvuXx4wjKli2dpfXXqVMTGxsrGpp0xNCwPKedD1CnXlsSE9WDqTp36U90dMxH16FQKLBbt4RuPQYSFhbJlctO2DucITg4RFPm++EDiYmJo3ZdU2xserFs6WwGfTsm3e2/ffuWzuY2vHr1Lzo6OrifP86pU25cvebDpcteODq54Hr24x9E3ufqniyXQxq5YmPiqJOUa+nS2XyblGuAjRWNknKdcj5A3UzUyzq7raxZsznDelcoFKxbt5gePQYRFhbJ5UuO6my3PmQbPvwbYmLjqFvXFJv+vVi65Ge+HTyWOrXVdWaSlM3ZeT/16rWjTu2ajPh+IF+3seDdu3gcHPbi7HyOu3cfAGC3PnPZMkOhUDDv1+l8338cTyKecPjMLs6ddufenQeaMpHhUcyasJDvxw7OkW1+CuseXRjUtxc//7Iy17elUChYtXoB1r2GEh4ehZv7cZycXLl9666mzHdD+xMbG0fjRh3p28+Chb/MYPjQCQQH3aFDW2tUKhXly5fl4hVHnJ1cUalULP9tHi5n3flu8Dh0df+PvfMOi+L6/vB7WcESewlS7C3RxAr2XlCaoKJ+YzQxRRNTrGg0dqOJKfaYxN5jLwiCgqJil66CYkWl2SgaKy7z+2OXdZcFBYSA+d33eXjYmXvunc+eO3Pmzpk7s6aUKFEsV9rmzptBT6dBxMYmEHDEA+89+7mgp+3jwf1ITk6h0fudcHNz4oeZ4/n4o2+JjIiiXZueGm2VK3HypDfeezTaNqzbzpK/1rJs2ZxXbj+vYwWQZZtZxYo5c/9izty/AHBy7MaI4UNISko20pmXsePixSvY2Nrp2r8eHcwuDx9de9mNHRn9OXvOFPq6fkJc7C18D25jr7c/F6Ou6Gw+/Kgvycn3ad7EDtc+DkyZ7s6QT0YBEH3tBp3auRq1u3jRSo4dOYWpqSk7dq+mS9f2HNgfkGNtCxbMxMFRE+OOH/PCy8uPC/oxbvD/SE5Opn6DdvTt25NZM79n4KCvePLkKdOn/0aD+vVo0KCeUdsuLj345+HDHOnJSuNPv02mn+tnxMfdYu/BLfj6HDTw34BBbiQnp9CqaQ9cejswaZo7X3w6GpVKxeKlv/DNF98ReS6KcuXKkpr6/LX1zJ4zhX6unxIXe4t9B7eyL0N/DvjIjeTk+7Rs0h3XPg5Mnj6GoZ+MBuD6tRt0adfLoM2H/zw0WOd7eDt7PP1ypW3WrxP5oNcQ4uNu4e2/GV+fg1zS0/bBoD6kpNynbTN7eva2Z+K00Qz7zB2VSsXCJbMZ8eUEra/KkJr6nKdPn2HXvo+uvs/BLXh75VxbgSHnnP//5cjRUyTqnTQy8sH/erFrlw83b8YBcOfOPV3ZgAG9OXHMi6BAX/5Y/DMmJsYu7+ncnS1bPHj27BnR0Te5ciWa5rY5y8g1t23ClSvRXLt2g9TUVLZs8aCnc/cM27Fj3bqtAGzfvofOndq+cvsPHz4CwNS0CEVMTVEUzS2ksLAIrl+PybGuzVs8cM6gyzkLXc7O3dn8mn55Gba2jY185qyXJTXStmMPnXTa7Ix8ZmvbmHfeqc2pU6E8fvwEtVrNkYCTuLj0yDPN+jRs2oAb124Scz2W1NTneO/0o0uPDgY2sTfjuRh5uVDc+rNp/D5lSpf6V7bVzKYRV69eJzr6JqmpqezY5oWjY1cDGwfHrvy9YQcAu3b60KFjKwBd3wEUK1ZUt8+XKlWSNm1sWbtmCwCpqamkpDzIsTYbm0ZcvfJC27Ztnjg6dTOwcXTsxob12wHYudOHjh1bG2srWhRFr1uPHTtNUmLWcSqd/IgVL2szO7Gif38XNm3e9VKdeR07Onduy9Wr17lxI/aVPnsZTZs1JPrqda5Hx5CamsquHXuwd+xiYGPv0JnNf+8EwHPXPtp1aPXSNh8/fsKxI6cAzX52JjwSCyvzHGszinFbd2ce49ZrLpx27NhDp05tAHj06DHHjwfy5OlTo3bfeqsEI0YM4aefFuZYU0aaNGvItas3uHFd67/t3nR3MLxr0d2hM1s2egDg5bGPth1aAtCxcxsiz0UReS4KgKSkZF3yJrc01ep50Z/e9MjQnz0curDlb83+6rlrH21f0Z/61KhZjYoVy3PyeJa5xixp0ux9oq/e1PnKY4c33R06GdjY2Xdmq9ZXezx8db7q0Lk15yMu6vkqxchXNWpWpWKl8pw6HpxjbZK8JV8G50KIcUKI4drP84QQ/trPXYQQ64UQdkKIE0KIECHEViFESW15MyHEYSFEsBBinxDCIkO7JkKINUKImdrlT4QQF4UQh4E2enbOQohTQohQIcR+IYS5tu4lIUQlvbYuCyEqvu73rVOnJmXLluGA31ZOnfRh4EA3AN55pzb9+vakXQdXbGztUKvVDBjQ26i+pWVlbsbE6ZZjYuOxtKoMgKIo+Hhv5NRJHz7/7MMsNVhaZdKGZeUsbdRqNSkp96lQodxLt29iYkJQoC/xsWc4cCCA04GhOfKNpVVlYvTajo2Nxyqbuqwsjetmxy9fDfuEkGA/li2dQ9myZbLUZmVpQczNeL32E7C0sshgU5mYmPgX2u5rfWZloVsPEBuTgJWlBRGRUbRr14Ly5ctSvHgxevTojLX1i9uKw74cTHCQH0uX/PZSbdnBvHIl4mNv6ZYT4m9hblHptdr8r2BpaU5sjGHfWlgaDm4sLCvrbNRqNfdTHlBeO+2imU0jTgb6cPyUN6NGTEatVlO9ehXu3k3kj79+4cix3Sz6/UdKlCieC22ViYnNsN9lPCYszXU2mv3ugW5KiI1tYwKD9nEqcC8jRkzUDdazvf18iBXZaTMrihcvRne7juzYaThtL79iRzr9+7mwOcMFQXZjhz4WlubExiboluNib2FhYbivVbYwJ1avP+/ff0D58pr+rFrNGv8jO/HYs46WrZoZtV+6TCns7Dtx5PCJbOnRJ2N/ZepDPV+la3vVVMVpU8cyf/4yHj9+nGNNGbGweJs4Pf/Fxxn7z8LCnDg9/z24/4Dy5ctSs3Z1FGDj9mX4Ht7O18M/43WpbPliWwBxsQlUNtLztkF/pusBTX/uP7KDnXvW0SKT/uzl5ojHTh+j9dnSZmGoLT7ulpG2ypYv/Jnen+XKl6VmreqgKGzYtpS9h7YybPinRu279HFk945XT4srTChpSr7+FRT5lTkPANppP9sAJYUQpkBb4CwwCeiqKEpTIAgYrS1fBLgpitIMWAnM0muzCLABuKgoyiTtwH06mkF5N6C+nu1RoKWiKE2ATcA4RVHSgPVA+kiuKxCuKMrdjOKFEEOFEEFCiKC0tFfftitSREWzpg1xdvkIB8cBTJwwkjp1atK5U1uaNnmfkye8CQr0pXPnttSsUdWovhDCaF16tq59R1eat+iBk/NAhg0bTLu2LTLV8LI2Xm7z8rppaWnY2NpRrYYNtjZNMr29+TLyS1eHDH5pq/XLkiVrqfdOa5rZ2BGfcJtff5nyEm3G67KnTcmy7oULl/n1tz/w8d6Il+d6zpyN5PlzzW3WJUvX8s67bbCxtSMh4Ta//Dw5S23ZIhu+/f9KVvuUoY1xvXT/BQeF09LWnk4dejF6zJcULWpGkSJFaNS4ASuWb6Bdm548fPSYUZnMF8+dtuztdwBBgWHY2nSnQzsXxrh/leM5tvlxTGanzaxwcrLj+Ikggykt+aUzHVNTU5yc7Ni23Uu3LiexI290KtxKuE2TBp3o3K4XkyfO5q/lcyhZ6i2djUqlYumKuSz/ax3Xo199pzJ32ozrvazvGjasT61a1di9O28GcZlqJHv9XESlokXLpnw9ZCwuPT7E3qkrbdu3fE09mazMRvBQFLiVcJumDTrTtV1vpk6czZ/LfzPoTwDXPg7s3LYnz7QZ9SeZGqEqosK2ZVO+GToOV/tB2Dt2oW17w/GES297dm1/+bNtkn+H/BqcBwPNhBClgKfACTSD9HbAYzQD6WNCiDDgY6AaUA94D/DTrp8EWOu1uQQ4pyhK+oC9BXBIUZQ7iqI8Azbr2VoD+4QQZ4GxQAPt+pXAR9rPnwKrMhOvKMpSRVFsFEWxMTF5KzMTA2Jj49nne5BHjx5z714SR46epGHD+gghWLd+Kza2dtjY2tHgvfbM+GEuLi49dA8dNWvakNjYeKroZVetrSyIj9NkROPjNf/v3LmHh4cPtraNM9cQk0kb8beytFGpVJQpU5rExKSXbj+dlJT7HA44Tne7jq/0R8Zt6meOrawsiMumrphY47qZ+WWXnl9u375LWloaiqKwYsUGbLLwF2iye9ZVXmTKrawqEx+XYGxjbfFCW+nSJCYma7+XXl3rysTFa+quXr2JFi3t6dLVjaTEZN18cwNtK//Osi+zy6342wa3uitbmHM7weha8/8lsbEJWFkb9m1Chv0uTs9GpVJRukwpo2khF6Ou8PDRY+rXr0dsbDyxsQkEB4UD4LHLh0aNGpBTYmPjsbbKsN9lPCZiE3Q2mv2uFIkZtEVFXeHRw0fUz+EFc37Eiuy0mRX9+/U0mtKSriE/YgdAjx6dCA09y+3bL46XnMQOfeJiE7DSy8pbWpmTkHDbwCY+LgErvf4sXboUSUnJPHuWqrsoORMWQfS1G9SqXUNXb+6CH7h6JZolf67JlpaMZOyvTH0Ym6DzVbq2jPuaPi1bNKNJk4ZERR3H/8AO6tSpga/vllzpA4iLu2VwV8PC0pyE+NsZbF7c1VSpVJTS+i8u7hYnjgWSmJjM48dPOOAXQMNG9Xkd4mNvGdxBtbSqnEl/3jLoz1JZ9udNg/6s/149ihQpwpmwiNxpizPUZmFpzq1MtKX788W+lkJ83C1OHgsiKTGZJ4+f4O93hPf0fKXRpuJseGSutBUYafn8V0Dky+BcUZRUIBr4BDgOHAE6AbWAa4CfoiiNtX/1FUX5DBBAhN769xVF0Z8cdxzoJITQfwIrq8v7RcDviqK8D3wBFNPqugncEkJ0RjO4z929pQzs9txH2zYtUKlUFC9ejObNm3DhwiX8Dx6ldy8n3QOi5cqVpWpVKzw89uoG7MEhZ/D08qVfPxfMzMyoXr0KtWvX4HRgKCVKFKdkSc3FQYkSxenWtQMREVGZaggMCqN27RpUr14FU1NT+vVzwdPL18DG08uXQYP6AtCnjyMHDx3Trc9s+xUrlqdMmdIAFCtWjC6d2xGl9+BJdsioq38/F7wy6PLKQpeXly/9c+iXypXf1rXr6mKfpb8AgoLCjXzmleFBGC8vvxfaejtySKfNz8hngYFhALr+rlLFEldXezZv9jDS5uLS46XassPZ0Eiq1ayKVVVLTE2L4NCrG/77cvbA2H+VkOAz1KpVnWrVrDE1NaW3mxPe3oZvW/L2PsCADzXTzFx72ROgnTZQrZo1KpUK0PRhnTo1uH4jhtu37xIbG0/tOpqTbYeOrQ0eMM0uwcFnqFX7hTY3N2e89xi+8cLbez8fDtQ8pNWrlz2HM9VmRZ26NbmRjWc/9MmPWJGdNjOjdOlStG/Xkt27971SZ17EjnT693c1mtKSk9ihT2jIWWrUqk5VbX+69nZkr7e/gc1eb3/6D9A8EOjs2p2jAScBqFChnO45pGrVralZqzrXo28CMGHSSEqXKcnE8T9mS0dmaGJc9Rf90rdn5jFOOxWzt16My4qly9ZRo6YN9eq1pnOX3ly6dA07u3651hgWcpaatapRtZqVxn99HPD1OWhg4+tzkH4fuADg5NKdY1r/HTpwlHcb1KN48WKoVCpatbE1eHAzN4Rm1NPbgX0Z+nOftz/9Bmge4n15f1bT9SdAbzfHXGfNAcJCzlGjVlWqVNVoc+mdia/2HqSv1leOLnYcC9A8u3D4wDHebVCXYlpftWxjY/AgqUsfB5k1L0Tk59taAgB3NBnqs8BcNBn1k8BiIURtRVEuCyFKoMl0RwGVhBCtFEU5oZ3mUldRlPRLzBVAe2CrEKIXcApYIISoANwH+gLhWtsyQPpTPhnfL7cczfSWdYqiZGuy5vp1i+nQvhUVK5Yn+moQ02f8hqmpKaAJVBcuXGaf70FCQ/aTlpbGypUbdYF9yrRf8PHeiImJIDX1OcOHTzR6ACky8iLbtnlyNvwgz9Vqho+YSFpaGubmldi2dQWgmTqzadMu9vkeylSjWq1mxMhJeO/5G5WJCavXbCYy8iLTproTFByOl5cfK1dtYs3qhVyIPEpSUjIDBn710u1bWJizcsV8VCoTTExM2LbNkz3emkHEN19/ivuYr6hcuRKhwfvx2evPF1+OzVLXngy6pk51J1hP1+rVCzmv1fWhnq6t2zw58xK/qLR+8dX6ZfZPk2jUqD6KohB9PYavvvouy35Vq9WMHDmZPV4bMFGZsGb1ZiLPX2TqFHeCQzTaVq3axOpVC4iMPEpSYjIDB2m1ndf4LDzcH/VzNSNGTNI9XLN501IqVCin6e8RE0lOTgHgpx8n0qhRAxRF4fr1m3z19eu9rkqtVvPD+F9YsXkhJioV2//ezeWoq3z73RecCzvPwX0BvNe4Pr+v/oXSZUrTya4t34z7Auf2/V9ru7ll7NTZBIaeITn5Pl1cB/LVZ4Pok+EBv7xCrVbjPmY6O3atRqUyYf26bVw4f4nvJ40kNOQsPt4HWLdmC0uXzyE03J+kpGQ+HTwCgJatbBg15gtSU5+jpKUxZtRUErVvBRo3ZjrLV8zD1MyU6Gs3+XrYuFxpGzN6Krt2r0WlMmHd2q2cP3+JSZNHERJyFu89+1mzejPLV8wj/OxBkpJSGPzRtwC0am3LmDFfkvr8OWlpaYwaOVn3xqJVqxfQrn1LKlQoR9Sl48yaOV/38GrG7ed1rAAybRNeHitcXezx2x/Ao0fGc5fzI3aAZo571y7tjWJDTmJHRp0T3GewZcdyTFQqNq7fTtSFy3z3/XDCQs+xz8efDeu28cfSXzkd6ktSUgpDP9W8qaVVG1u++344z5+rSUtT4z5qKslJKVhYmjN67DAuRl3BP0DzIOmKZetZvzZnr+lNj3FenutRqVSsXrOZ8+cvMmXKGEKCz+C1x49VqzexauV8IiOOkJiYzKCPXryyMirqOKVLlcLMzBRn5+44On1o8KaXvECtVvP92Jls3L4clcqEjet3EHXhMuO+/5aw0HP4+hzk73Xb+H3Jz5wI2UtyUgpffKp5tWRKyn2WLF7NXv+tKIrCAb8A9vsefm09E9x/YNOOFVo923V6wkPPsS9dz9JfOBm6T6tH86aWlm1sGff9t6ifq1GnqRk3ahrJSSm6tnv2smeA29DX0jZp3Cz+3r4UE5UJmzfs5OKFK7hP+IbwsAj8fA6yad12Fv41m6PBPiQnpfDVZ+46Xy39Yw3eBzajoODvd4QDvi+SOc6u3RnUb1iutRUUyn/0bS0iv+aoCiG6AHuBsoqiPBRCXAT+UhRlrjZz/TNQVGs+SVGU3UKIxsBCNIPrIsB8RVGW6b9KUQgxHaiLZu74x8AEIB4IA1SKonwjhHAB5qEZoJ8EbBVF6ajVZQrcA5orinLhVd+jiJmVnMSbQzKbsldYyGzuYmGgZhmLVxsVEGcjN7/aqICoWL3bq40KCPVrvjUiv3jy/FlBS8iSwnl0aihbvGRBS8iU+08fFbSELClXrHD6DArv8zlFTFQFLeGlxCZFFKrD9J5zh3ztyAqehwvk++Zb5lxRlAOAqd5yXb3P/oBtJnXC0GTHM67vqPd5ql7RKjKZN64oigfgkYW0RmgeBH3lwFwikUgkEolEUkgpnDmQ1+aN+BGivEIIMR4Yxos3tkgkEolEIpFI3kD+q9Na/l/9CJGiKLMVRammKMrRgtYikUgkEolEIpFk5P9V5lwikUgkEolE8h9BZs4lEolEIpFIJBJJfiIz5xKJRCKRSCSSNw4551wikUgkEolEIpHkKzJzLpFIJBKJRCJ545CZc4lEIpFIJBKJRKJDCNFDCBElhLisfWV3Zjb9hBCRQogIIcTfr2pTZs4lEolEIpFIJG8cBZ05F0KogMVANyAGCBRC7FYUJVLPpg6aX7NvoyhKkhDi7Ve1KzPnEolEIpFIJBJJzmkOXFYU5aqiKM+ATYBLBpshwGJFUZIAFEW5/apGZeb8FYiCFpAFJiaF97pKnVaIJ4EpSkEryJTWJaoVtIQsqVi9W0FLyJK70X4FLSFLqtR2LGgJmVLc1KygJbyRPEp9WtASMkUU2rMU+JatXdASsmQDJQtaQqasSQ4raAlvFkr+7v9CiKHAUL1VSxVFWaq3bAXc1FuOAVpkaKautq1jgAqYpijK3pdtVw7OJRKJRCKRSCSSDGgH4ktfYpLZ1UHGLGARoA7QEbAGjggh3lMUJTmrRuXgXCKRSCQSiUTyxlHQc87RZMqr6C1bA3GZ2JxUFCUVuCaEiEIzWA/MqtHCOzdCIpFIJBKJRCIpvAQCdYQQNYQQZsD/gN0ZbHYBnQCEEBXRTHO5+rJGZeZcIpFIJBKJRPLGoaQV7DMXiqI8F0J8A+xDM598paIoEUKIGUCQoii7tWV2QohIQA2MVRTl3svalYNziUQikUgkEokkFyiK4g14Z1g3Re+zAozW/mULOTiXSCQSiUQikbxxFII55/mCnHMukUgkEolEIpEUEmTmXCKRSCQSiUTyxqHk83vOCwqZOZdIJBKJRCKRSAoJMnMukUgkEolEInnj+K/OOZeDc4lEIpFIJBLJG0dBv0oxv5DTWnKInV1Hzp0L4HzkUcaO/dqo3MzMjA0b/uR85FGOHfWkWjVrXdm4cd9wPvIo584F0K1bB4N6JiYmBJ7ex66da3Trli75jeAgP0KC/di0aSlvvVUi+zq7deTsmUNERhzB3f2rTHWuX/cHkRFHOBKwW6ezfPmy7Nu3mXt3LzB/3g86++LFi7Fr52rOhB8kNGQ/M38Yn20t+nS360jEuQAuRB5lXBb++3vDn1yIPMrxDP77btw3XIg8SsS5AOy0/rO2tmS/71bOnjlEeJg/337zWY705Ed/Xrp4ktCQ/QQF+nLyxIu3KzVsWJ8jAbsJDdnPzp2rKVWqZI60pvNeh8b8eGABPx1ahMMwV+Pv9JkTM/3mMd1nDu4bplLBqqKubPmVzUzz/pVp3r/y7bLvcrV9fbp0bU9QiB+h4f6MGv2FUbmZmRmr1iwkNNyfAwe3U7WqFQBNmzXkyHFPjhz35OgJL5yc7XR1ypQpxdr1vxMY4svp4H3YNm/y2jpfxaQf59Le8X+4Dvwy37cF0KlLW44GenMiZC/fjPzcqNzMzJQlK+dyImQv3vs3UaWqpa7s3QZ18fLdyOETnhw85kHRomYAjJ80guBz/lyJCcq1rs5d2nEiaC+nQ30ZPmpIprqWrZrH6VBf9h7YQhVtf1apasWNhHAOHtnFwSO7+HXedF2dzduXc/CoB0dOevHrvOmYmOTutJPX2ooXL8bfW5ZwPNCHIye9mDxtTK50AXTr1oHQsAOcOXuIMWOGZaLNjDVrf+fM2UMcOryLqlU1caRz57YcPebJ6dN7OXrMkw4dWhnV3bJ1GYGB+3Kt68yZg0REBGR5Hli3bjEREQEEBHhkOA9s4u7d88ybN8OgjpubM4GB+wgJ2c+sWd/nSldGSnZoSr0Df1Lv0BIqDXMzKi/n1oX6weup472AOt4LKN9fEy+K1a9BrR2/Utd3MXV8FlLGqW2e6MmKuh0a4X5gDmMPzaPjsJ5G5S0+7MrIvT8zwvsnvtw6lbdrW+W5hsIaOyR5y782OBdCRGt/GSnj+uP5vY28wsTEhIULZuHsPJCGjTrxv/6uvPtuHQObTz/5gOSkFN6t35YFC5fx448TAXj33Tr07+dCo8adcXL6kEULfzQ4SQ3/9nPOX7hk0NYY92k0s+lG02bduHkjlq+++iTbOhcsmElPl49o1Lgz/fu58M47hjo/Gfw/kpOTqd+gHQsXLWfWTE2QffLkKdOn/8b48TON2p03fwkNG3WieQt7WrW2pbtdx2zp0de1cMEsnJwH8n6jTvTPwn9JSSm8U78t8xcu4yc9//Xr50LDxp1x1PPf8+fPGTtuOu837Eibts4MGzbYqM1X6cmP/uzarS82tna0bOWgW7fkr1/5fuKPNGnaFY9dPpmexF+FMDFh4IzPmTd4FpO6jaJFz7ZY1rY2sLkReY0Zzt8x1X4MQT4n6DthkK7s2ZNnTHMYyzSHsSwa8nOOt6+PiYkJc+ZOw633pzS36U6fvs7Ue6e2gc1HH/clOTmFJo0688fiVUz/QXNBcD7yIh3budKutTN9XD9h/sKZqFQqAGb/MoX9fgHYNrWjTUsnLkZdfi2d2YxSgsEAACAASURBVMHVoRt/zTXe5/MDExMTfvptMgPchtK+hTO93BypW6+Wgc2AQW4kJ6fQqmkPlvyxlknT3AFQqVQsXvoL40ZPo0MrZ3o7fUxq6nMAfPcewr5L/9fSNXvOFP7n9jltmjvSq4+Tka4PP+pLcvJ9mjex468/VjNluruuLPraDTq1c6VTO1fGjpqqW//Z4BF0autCu5ZOVKxYjp69ehQabYsXraS1rT2d2/WieYumdOnaPlfa5s6bQS/XwTRr2o2+fXvyTobj4OPB/UhOTqHh+x35fdEKfpipSW7cu5eEm9tnNG/eg6FDxrB8xTyDej1duvPwn0c51pSua8GCmbi4fEzjxl3o16+n0Xlg8OD+JCen0KBBexYtWs7MmROA9PPAHMaPn2VgX758WX766Xvs7T+gadOumJtXpFOnNrnSpycUqxlfcm3wNC52+5qyPdtTtHYVI7NkryNcchjBJYcRJG72BSDt8VNujp7LRbuvufbxNCynDMGk9FuvpycLhInAdcYnrBz8M3O7udOoZ2ujwXeYxzHm9/iOBQ4TOLzEC6fJg7JoLXcU1thRkChK/v4VFP/K4FwIocqqTFGU1v+GhryguW0TrlyJ5tq1G6SmprJ5iwfOzt0NbJyd7Vi3bisA27fvoXOnttr13dm8xYNnz54RHX2TK1eiaW6ryQhaWVlgb9+FlSs3GrT14ME/us/FixdDyeaeYmvb2EDnlq27cdbLTOp0rt8GwI4de3QB9tGjxxw/HsiTp08N7B8/fsLhwycASE1NJSz0LFbWFtnSk05G/23Z4kHPDP7rmYX/ejp3Z0sm/ktIuE1o2DkA/vnnIRcuXMLKsnKu9ORVf2ZF3bq1OHLkJAD7DxyhVy+Hl9pnRs3Gtbl9PYE7N2+jTn3OKc9jNLazNbC5cCKCZ0+eAXA19BLlKlfI8XayQzObRly9ep3o6JukpqayY5sXjo5dDWwcHLvy94YdAOza6UOHjprM4OPHT1Cr1QAUK1ZUt2+XKlWSNm1sWbtmC6DZ11JSHuSLfn1sGr9PmdKl8n07AE2aNeTa1RvcuB5Damoqu7Z7092hs4FNd4fObNnoAYCXxz7admgJQMfObYg8F0XkuSgAkpKSSUvTTLoMCQrn9q07udbVtFlDoq9e53q0VteOPdg7djGwsXfozOa/dwLguWsf7TLJ9GbknwcPAShSpAimpqa5OuPlh7bHj59w7MgpQLOfnQmPxMLKPMfabGwac/XKi+Ng2zZPnJwM462Tox0b1m8HYOdObzp21Jz2wsMjSIi/DUBk5EWKFi2KmZkmm/nWWyX49tvP+fnnRTnWBMbnga1bPTM9D6zXnQe8jc4DT58+MbCvUaMqly5d4+7dRAD8/Y/i6mqfK33plGhch2fX43l28xZK6nOSPQMobdciW3WfXYvjWXQ8AM9vJ/L8XgpFypd+LT1ZUaVxbe5dTyDx5m3UqWrCPU9Q387GwObpP491n81KFM3z0V1hjR2SvOeVg3MhxDghxHDt53lCCH/t5y5CiPVCiA+EEGeFEOeEED/r1ftHCDFDCHEKaKW3vrgQYq8QYki6nfZ/RyHEISHENiHEBSHEBiGE0JY5aNcdFUIsFEJ4addXEEL4CiFChRBLAKG3nV1CiGAhRIQQYqh23WdCiHl6NkOEEHOz6yxLq8rExMTplmNj440GgpZWlbmptVGr1aSk3KdChXJYWRrXtbTS1J0zZzoTJszUHSj6LF82l5ibYdSrV5vFi1dmT6flCw1Z6tTTo1aruX//ARUqlMtW+2XKlMbRsSsHDx7Llr1um1aGumJi47HMpv8yfqcYPf+lU62aNY0bvcep06HZ1pMf/akoCj7eGzl10ofPP/tQZxMREaU7Obr1caKKtSU5pax5eRLj7uqWk+LvUc68fJb27fp15uyhF/4wLWrGlN0/M3HnjzTJMKjPKZaW5sTGxOuWY2MTsLA0HNxYWFbW2ajVau6nPKC8dj9rZtOIk4E+HD/lzagRk1Gr1VSvXoW7dxP5469fOHJsN4t+/5ESJYq/ls7ChoXF28TFJuiW4+NuYWGRwW8W5sTFvvDbg/sPKF++LDVrV0cBNm5fhu/h7Xw9PGfTuF6qy9KcWD1dcbHGuipbmBOrp+v+/QeUL6/pz6rVrPE/shOPPeto2aqZQb0tO5Zz/spx/vnnIbt35XyKRn5qAyhdphR29p04ok1A5ARLS3NiYg1jQcbjQN8mq3jr6mrPmfAInj3TXFhPmTKGhQuX8+iR4QA5+7oyiVFGunJ2Hrhy5Tp169aiWjVrVCoVzs52WOcijuljal6BVL2Ylhp/D1Nz44RCGfvW1PFZSNU/xmNqYXyDvHijOgjTIjy7nmBUlheUMS9HctyLX1xPib9HGXNjX7Ua1I1xh+fjMH4AHtPWGJW/DoU1dhQkSprI17+CIjuZ8wCgnfazDVBSCGEKtAUuAT8DnYHGgK0QIn0S7FvAOUVRWiiKclS7riTgCfytKMqyTLbVBBgJ1AdqAm2EEMWAJYC9oihtgUp69lOBo4qiNAF2A1X1yj5VFKWZVvNwIUQFYBPQU6sf4BNgVTZ8AID2WsGAjNnszG2yruvg0JU7t+8SEno2021+PmQ0Vas15cKFS/TrazzHLfc6jetlJzOvUqlYt/Z3Fi9exbVrN7KlJ2e6cua/dN56qwRbNi9jtPtUgzsOBaGnQ0dXmrfogZPzQIYNG0zbtpos0JChoxn25WBOnfShZKm3ePYsNVs6c6o5nZau7ajesBZ7l3ro1o1t/SUzen7H0uHz+WDKJ1SqmvNM4cu1ZLQxrpeuNzgonJa29nTq0IvRY76kaFEzihQpQqPGDVixfAPt2vTk4aPHjBrz78wD/7fI1G9kb78rolLRomVTvh4yFpceH2Lv1JW27Vvmn65sHQ8KtxJu06RBJzq368XkibP5a/kcSpZ6Mb2gX+/Pea9uW4oWNaNdh5zrzU9tKpWKpSvmsvyvdVyPjskXbZkdCPo2775bhx9mjufbbzXTCxs2rE/NWtXw3J27uebZ1ZWTeAKQnJzC8OETWbduMQcObOP69RieP3+ea41aEcbrMmi4v/80F9p+xiX74fxzLIwqc0YalBepVI6qc0cTM3ZB/s1FyEa8Azixzo9fOozEZ/bfdPm2Vx5LKJyxQ5L3ZGdwHgw0E0KUAp4CJ9AMeNsBycAhRVHuKIryHNgApE/aUwPbM7TlAaxSFGVtFts6rShKjKIoaUAYUB14B7iqKMo1rY3+3I/2wHoARVH2AEl6ZcOFEOHASaAKUEdRlIeAP+AkhHgHMFUUxWhULIQYKoQIEkIEpaU91K2PjYk3yBJYWVkQF3/LoG5sTLwuI6pSqShTpjSJiUnExBrXjY+7RevWNjg52XHp4kk2rP+DTp3asGb1QoM209LS2LJ1N716OWbhNkNiY+MNsrKZ6oxN0OlRqVSULl2KxMTkV7b9xx8/c/nyNRb9viJbWgy2GWOoy9rKgvhs+i/jd7LW+g80t8u3bl7Gxo072bXLJ0d68ro/Ad13unPnHrs8fLC1bQxAVNQVHBwH0KKlPZs3e3D1anS2taaTlHCP8pYvskblLCqQfDvJyK5+m/dx+qYPCz+fzfNnL06e6bZ3bt7mwskIqjaokWMN6cTGJhhMbbKyqkxCBv/F6dmoVCpKlylFUob97GLUFR4+ekz9+vWIjY0nNjaB4KBwADx2+dCoUYNcayyMxMXdMrjrY2Fprpva8MImAUurF34rVboUSUnJxMXd4sSxQBITk3n8+AkH/AJo2Kh+3uiKTcBKT5ellTkJCYa64uMSsNLTVVqr69mzVJKSNP16JiyC6Gs3qFXbcN96+vQZe739sXcwnI5S0NrmLviBq1eiWfJn7rKcsbEJWFsZxgKj/tSzyRhvLa0qs3HTEoZ8PlqX8GjeoilNmrxP5Pmj7D+wldp1auCzd1MOdWUSozLo0rfJ7nnA23s/7du70LFjLy5dusrly9E50pWR1IS7mOrFNFOLCqTeTjSwUSc/QNHGscSNvhR/78WcfpOSxamxaioJc9bzKDTqtbS8jJSERMpavsjol7GowP1MYm864Z4naNDNJsvy3FBYY0dB8v82c64oSioQjSbLfBw4AnQCagEvS50+URRFnWHdMcBeZHZpp0F/orMazaseX+Udo2tXIURHoCvQSlGURkAoUExbvBwYzEuy5oqiLFUUxUZRFBsTkxcZlsCgMGrXrkH16lUwNTWlfz8XvLx8Dep6efkyaFBfAPr0ceTgoWO69f37uWBmZkb16lWoXbsGpwNDmTRpNjVq2lCnbks+HPgVBw8e4+PBwwGoVau6rl0nx25EZfPBuKCgcGrXrq7T2a9vT7y8/DLo9GPQQM1T8b17O3Lo0KunqEybNpYypUsxxn1atnRkJKP/+vVzwTOD/zyz8J+nly/9MvEfwLKlczh/4TLzFyx9LT150Z8lShSnZEnNPlOiRHG6de1ARITmhFGpkiawCyH4fsIIli5dlyO9ANfCL2Ne3YKK1m+jMi1CC+c2hPkFGthUbVCDj378goWfz+bBvfu69SVKv0URM83bU0uWK0WdZu8QfynnmcJ0QoLPUKtWdapVs8bU1JTebk54ex8wsPH2PsCAD3sD4NrLngDttIH02+IAVapYUqdODa7fiOH27bvExsZTu45m8NShY2uiLuT/A6H/JmEhZ6lZqxpVq1lhamqKax8HfH0OGtj4+hyk3wcuADi5dOdYgOZZhUMHjvJug3oUL14MlUpFqza2XIy6kie6QkPOUqNWdapq+9O1tyN7vf0NbPZ6+9N/gCYb6OzanaNaXRUqlNM9EF2tujU1a1XnevRN3nqrBObmmpudKpWKrnYduHTxaqHQBjBh0khKlynJxPE/5lhTOsHB4dSq/eI4cHNzZs8ew3i7x9uPDwf2AaBXLwcOH9a8B6FMmdLs2L6KqVN+4eTJYJ398mXrqV2rBfXfbUvXLn25fOka9j3+lyNdmvPAi/jWt69zpueBgbrzgAOHDr36/Qzpcaxs2TIMHTqIVas2vqLGy3kUfgmz6paYWpsjTItQ1rk99/1OG9gUqfRi+kjpbs15ckXTf8K0CNWWTCRphz8p3jmbZplTYsKvUKF6ZcpZV0JlqqKRcyvO+wUb2FSo/mLg/E7nJtyNztspNoU1dkjynuy+5zwAcAc+Bc4Cc9Fk1E8C87VvSEkCPgBe9vTKFGAy8AeQ3VdVXABqCiGqK4oSDeg/UhwAfAjMFELYA+lHcBkgSVGUR9oMue7ejaIop4QQVYCmQMNsagA087dGjJzEnj1/ozIxYfWazURGXmTqVHeCg8Px8vJj5apNrF69kPORR0lKSubDgZrXV0VGXmTrNk/OhB/kuVrN8BETM51jno4QgpUr5lO6dEkQgrNnIvn6mwnZ1jly5GS8PNejUqlYvWYz589fZMqUMYQEn8Frjx+rVm9i1cr5REYcITExmUEfvXiNYFTUcUqXKoWZmSnOzt1xdPqQBw8eMGH8cC5cuMSpk5rs9J9/rWbVquxnc9L9553Bf9OmuhOk5781qxdyQeu/AXr+27bNk7MZ/NemtS2DBrpx5mwkQYGagfXkybPx2ev/MikGevKyP83NK7Ftq+augqqIik2bduHrewiA//V35cthgwHYtcub1Ws2Z9t36aSp01g/ZTmj107CRGXC0S3+xF2KwXVUf6LPXiFsfxD9JgyiaIlifPWH5tVw92LvsmjIz1jUtubjH4eiKApCCLz/3Enc5dwPztVqNe5jprNj12pUKhPWr9vGhfOX+H7SSEJDzuLjfYB1a7awdPkcQsP9SUpK5tPBIwBo2cqGUWO+IDX1OUpaGmNGTSXxniYLNW7MdJavmIepmSnR127y9bBxudaYXcZOnU1g6BmSk+/TxXUgX302iD4ZHg7OK9RqNd+PncnG7ctRqUzYuH4HURcuM+77bwkLPYevz0H+XreN35f8zImQvSQnpfDFp5q+TEm5z5LFq9nrvxVFUTjgF8B+38MATJ7uTi83R4qXKE5IhKaN32YvzpGuCe4z2LJjOSYqFRvXbyfqwmW++344YaHn2Ofjz4Z12/hj6a+cDvUlKSmFoZ+OAqBVG1u++344z5+rSUtT4z5qKslJKVSqVIF1m/7EzMwMlcqEowEnWb0yZxng/NJmYWnO6LHDuBh1Bf8AzYOkK5atZ/3abTnWNmb0FDx2r0WlUrF27RbOn7/EpMmjCAk5i/ee/axZvYXlK+Zy5uwhkpKS+fijbwH44suPqFmrGuMnDGf8BE1SpqfzIO7cufeyTWZb18iRk/H0XIdKpWKN7jwwmuDgs+zZ48fq1ZtZuXI+EREBJCYm89FH3+jqR0Udo5TeecDJaSAXLlxizpxpvP++JuP644/zuXz5WlYSsik0jbgpf1Fz7XRQmZC0ZT9PL93AfNSHPD57ifv7T1PxE2dKd22BolajTn5AjPsCAMo4tqVk8wYUKVeKcm6aOzI33efzJPI1NWVCmjoNjymr+WztBExUJgRuOcStSzF0G+VGzNlrnN8fTOuP7ajT5n3Uz5/zOOUhW8b8macaCmvsKEgK8o0q+YnIzjxjIUQXYC9QVlGUh0KIi8BfiqLMFUIMACagyXB7K4oyTlvnH0VRSuq1EY1mOsw9YCVwR1GUcel22my3u6IoTlr734EgRVFWCyGcgV+Bu8BpwFxRlA+188g3AhWBw0BvoBnwANgFWAFRaOapT1MU5ZC27fFAY0VRXpmKMDWzKpRdn9t3Bf8bqF9y0VHQFNafKxhk+eq3XhQU2+9m7wHbguButN+rjQqIKrWzNw3t30b9X/1JvXzmUerTVxsVAIU53p6u3KigJWTJBnL3OxP5zZrksIKW8FISks8XqtPotUbd8nWMViPcr0C+b7Yy54qiHABM9Zbr6n3+G/g7kzolMyxX11v8JKOdduB8SG/9N3r2BxVFeUc7HWYxEKS1uQfovxtqlN7nl73fqS0w7yXlEolEIpFIJJJCjPyF0IJliBAiDIhAM2VlSW4aEUKU1Wb9H2svOCQSiUQikUgkkkJDduecFyiKoswjDzLdiqIkA3VfaSiRSCQSiUQiKdQoisycSyQSiUQikUgkknzkjcicSyQSiUQikUgk+vxXn2+XmXOJRCKRSCQSiaSQIDPnEolEIpFIJJI3jjQ551wikUgkEolEIpHkJzJzLpFIJBKJRCJ545Bva5FIJBKJRCKRSCT5isycSyQSiUQikUjeOP6rvxAqB+evoIiqcLpIUZSClpAlZYu9VdASsiStkPpt252QgpaQJYXVZwBVajsWtIQsuXl5T0FLyJRm731Y0BKyRFB4T7R73y9a0BIypX7YzYKWkCXzMCtoCVlShOcFLSFTPirbuKAlvFEU4tPTayGntUgkEolEIpFIJIWEwpkWlkgkEolEIpFIXsJ/dVqLzJxLJBKJRCKRSCSFBJk5l0gkEolEIpG8ccgfIZJIJBKJRCKRSCT5isycSyQSiUQikUjeOOSPEEkkEolEIpFIJJJ8RWbOJRKJRCKRSCRvHPI95xKJRCKRSCQSiSRfkZlziUQikUgkEskbh3xbi0QikUgkEolEIslXZOZcIpFIJBKJRPLGId/WIjGiW7cOhIf7c+7cYdzdhxmVm5mZsW7d75w7d5iAgF1UrWoNQOfObTl2zIvAwH0cO+ZFhw6tdXWmTRvLpUsnuHMn8rW1nTlzkIiIANzdv8pC22IiIgIICPCgWjWNtvLly7Jv3ybu3j3PvHkzDOq4uTkTGLiPkJD9zJr1fa50de7ajpPBezkd5sfwUUMz0WXK8lXzOR3mxz7/rVSpamVQbmVtQXRcKF9/+6lu3dBhH3HkpBdHT+3hi68+zpUugC5d23EqZB9BYfsZMTozbWasWD2foLD9+Plvy1Tbjfgwvhn+mcF6ExMTDh31YOPWpbnW1rVbe4JD9xN2xp9RY77MVNuqNQsJO+OP/6EdVNVqa9asIUdPeHH0hBfHTu7BydlOV+dsZAAnTvtw9IQXh4545FpXSNgBws8eZHQWutasXUT42YMcPLzzhS6bRhw/uYfjJ/dw4qQ3zj1f6Prjr5+5Fh3I6cC9udKUTqcubTka6M2JkL18M/LzTLSZsmTlXE6E7MV7/yaqVLXUlb3boC5evhs5fMKTg8c8KFrUDIDxk0YQfM6fKzFBr6Utu0z6cS7tHf+H60Bj3/4btOnUkt1HN+F1YiuffjPIqLxZy8Zs9l1NSMwRujl10q2v16AO67yWsuPwBrb5r6O7S5c81dW6Uws8jm7E88SWTHU1bdmYTb6rCI4JoGsGXWu9lrLj8Hq2+q/Nc10ARVvYUunvNVTatJ63Bn5gVF7cvjtve+6k4qplVFy1jOJODrqyUsOGUnHtSiquXUmxzp2M6r4uXbq253SIL8HhBxg5+gujcjMzM1asWUBw+AH8DhrHOGtrC24mhBvFuNflvQ6N+fHAQmYf+h2HYb2Myu0+c2am33xm+Mxl7IapVLCqpCtbcWUL071/Y7r3bwxfNj5PdQE06NCYHw4sYNahRfQY5mpU3u0zJ6b7zWOqz2+M3jCF8lYVdWXlLSsycu0kZuyfx3S/eVSwrmRUP6+o26ERYw/MYdyheXQc1tOovOWHXRm192dGev/EsK1Tebu2VSatSAoLhWJwLoQYLISw1FuOFkJUfFmdXG7HWwhRVvtnPGLNASYmJsyf/wMuLh/TpElX+vbtyTvv1DGwGTy4P0lJKbz3XgcWLVrBrFmawHHvXhJubp9ia9udIUNGs3LlPF0db+/9tGvn8jrSMDExYcGCmbi4fEzjxl3o1y9zbcnJKTRo0J5Fi5Yzc+YEAJ48ecr06XMYP36WgX358mX56afvsbf/gKZNu2JuXpFOndrkWNfPc6bSv88Q2tg60NvNibr1ahnYfPhRX5KTU2jeuBt/LV7N1OljDcpn/vQ9B/wCdMvvvFuHQR/3w66TGx1a98Sueydq1qqWI13p2n6ZM41+vT+nla09fdycqFevtoHNwI/cSE6+j03jrvy5eBXTZhhq+3H2RANt6Xz51cdcjLqSY0362ubMnU6fXp9g26w7bn2dqfeOobaPPu5HcvJ9GjfszOLfVzL9h+8AiIy8SIe2LrRt5URv18EsWDQTlUqlq+doP4C2rZzomIt9zsTEhLnzZtDbdTA2Te20x4Chro8H9yM5OYVG73di8aIV/DBTcwxERkTRrk1PWrd0xNX1YxYunKXTtWHddlxdB+dYT0ZtP/02mQFuQ2nfwplebo5G+9qAQW4kJ6fQqmkPlvyxlknT3AFQqVQsXvoL40ZPo0MrZ3o7fUxq6nMAfPcewr5L/9fSlhNcHbrx19yZ/9r29DExMeH7n8YwbMBoXNt/gH2vbtSsW93AJj42gUkjfsBnp5/B+iePnzDx2xn07vAhwz4YxbgZIylVumQe6nLnqwFj6NV+AD16dTXSlRCbwOQRMzPVNenbGfTuMJCvPhjN2Bkj8kyXVhylR48g0X08dwYOpnjXLhSpbhyPnvgf5O4nQ7j7yRAee3kDULRVS0zr1uHuJ59zb+hXvDWgP6JEiTyUZsKvc6fRt/dntLTpQZ++TkZxZNDHfUlJTqFZoy6aGPfDOIPyWT9PZH8mMe51ECYmDJoxhHmDZzGx20ha9GyLZW1rA5sbkdeY4TyOKfajCfI5Sb8JLy7Inj15xlQHd6Y6uLNwyOw81zZgxmcsGDyLKd1G0bxnGywy0TbL+Tum27sT7HMSNz1tn879hn1LdzOl6yh+dJnAg7spearvhU5BrxmfsGLwz8zp5k7jnq2NBt+hHseY1+M75jtM4PASL5wnG1/UvokoSv7+FRSFYnAODAYsX2WUHYQQWU7VURTFQVGUZKAs8FqDc1vbxly5Ek109E1SU1PZutUTJ6duBjZOTt3YsGE7ADt2eNOxo2YwGx4eQXz8bUAzeCpatChmZprM3OnToSQk3H4daTpt167d0Glz1suYAjg727F+/TadtvSB9qNHjzl+PJCnT58Y2NeoUZVLl65x924iAP7+R3F1tc+RrqY2Dbl29TrXtT7buX0P9o5dDWzsHbuwaeNOAHbv2ku7jq30yrpyPfomURcu69bVrVeL4MBwHj9+glqt5vix0zhm6Ifs0CyDth3b92DvZJhVc3Dsyqa/dwDgsWsv7fW0OTh1JTr6JhfOXzKoY2lZmW7dO7JuzZYca0rHxqYRV69e1+1r27d5GX1HR6eubNTua7t2+tCxo+ZuTLpfAIoVLZqnwcbGphFXr7zQtW2bp7Eux25sWK/RtTObuo4dO01SYvJraWvSrCHXrt7gxvUYUlNT2bXdm+4OnQ1sujt0ZstGzR0DL499tO3QEoCOndsQeS6KyHNRACQlJZOWlgZASFA4t2/deS1tOcGm8fuUKV3qX9uePu81qc+NazHE3ojjeepz9u7aT6fu7Q1s4m4mcOn8FZ1/0rl+9SY3rsUAcOfWXRLvJlGuQtk803Uzg66O3dsVuC4A03ffQR0ThzouHp4/5/F+f4q2zV4So0j1ajwLCwd1GsqTJzy/fIWiLZvnmbZm2jiii3Hb9uBgFH+7snGDJv567NxLhwwx7vo14xj3utRsXJvb1xO4c/MW6tTnnPY8ShM7WwObCyfO8ezJMwCuhF6kXOUKeaohK2o0rs2d6wncvXkbdepzAj2P0djOxsAm6kSETtvV0IuUq1weAIva1pioVJw/egaAp4+e6OzymiqNa3P3egKJN2+jTlUT7nmCBhl0Pv3nse6zWYmiKP/VdxD+R8jV4FwIMU4IMVz7eZ4Qwl/7uYsQYr0Qwk4IcUIIESKE2CqEKKktnyKECBRCnBNCLBUa3AAbYIMQIkwIUVy7mW+19c8KId7R1n9LCLFS20aoEMJFu36wdjuegK8QwkIIEaBt75wQop3WLj0jPxuopS3/NTc+sLSsTExMvG45NjYeK6vKmdjErh92mgAAIABJREFUAaBWq7l//wEVKpQzsOnVy4Hw8AiePcu7g1Z/u+naLC3Nc6xNnytXrlO3bi2qVbNGpVLh7GyHtXXOrqcsLMyJi0nQLcfFJWCRQZeFhTmxWr+m6ypfvhwlShRn+Kgh/Dr7dwP785GXaNXGhnLly1K8eDG62nXA0toiR7o0261MbOyL/oyLTcDCIoM2S3NitfrVajX3U/6hfAWNthGjhvLLT4uM2v3x54lMm/yL0SAhR9oy7GtxsfFYZqItJqPftP1pY9OIU4F7OXHah5HDJ+kGxYqisGv3Gg4f9WDwJ//LsS5Ly8rExOofAwlYWmY8Bsx1Nmq1mhS9/czGtjGBQfs4FbiXESMm6nTlBRYWbxMX+2Jfi4+7ZdyfFubE6Wl7cP8B5cuXpWbt6ijAxu3L8D28na/z+Bb+m4K5RSVuxb1IFNyKv83bFjm/Lf9ek/qYmppyMzo2T3S9bVGJhLhbuuXb8Xcwz5Wud/NUF4CqUkXUt1/4LO3OHVSVjG8CF+vQnoqrl1P2h2mYvK3Rnnr5CkVbtICiRRFlSmPWtDGqt/NuGoQmfmWIcUbnhQzx1yDGfcHPmcS416WceXkS4+7qlhPjEylnnvXgu32/Lpw9FKJbNi1qxpTdPzNp5080scu7ixmAsublSYy7p1tOik+k7Eu0te3XhXOHQgEwr2nB4/sPGfaXO5P3/ILbhEEIk/zJh5YxL0eKns6U+HuUNjc+n7ca1I3vDs/HYfwAdk9bky9a/m3SFJGvfwVFbh8IDQDGAAvRDKyLCiFMgbbAWWAS0FVRlIdCiO+A0cAM4HdFUWYACCHWAU6KomwTQnwDuCuKEqQtA7irKEpT7fQTd+BzYCLgryjKp0KIssBpIcR+raZWQENFURKFEGOAfYqizBJCqICM9wbHA+8pitI4sy8nhBgKDAUoUqQ8RYoY3/YUmfRZxitRkYmRvs2779Zh5szxODkNzExGrnnVdrNro09ycgrDh09k3brFpKWlcfJkMDVqVP13dKHw3ffD+Wvxah4+fGRQduniFRbOW8b2Xat4+PAREWcvoH7+PEe6NNs1Xpddn42fOJw/f19lpM2uRyfu3LlHeFgEbdrm/qSRLW1kagRAUFA4LWx7ULdeLZYs/Q0/30M8ffoMuy59SUi4TcVKFfDwXMvFi1c4fiwwB7pebz8LCgzD1qY79erVYsmyOfju0+jKC7Laj16tDYqoVLRo2ZQenfry+PETtnqsIjwsgqMBJ/NE2xtDDmNEZlR8uwI/LprCpOE/5FmmLjvHQ3Z0zVo0hUnDZ+ZtBjFzcQaLT46d4PF+f0hNpYSLM2UnjidxxBieBQbx9N16VPzrd9KSk0k9F4mizv1FvbG0bPRnljFuBH8uNo5xeSTs1bq0tHJtT/WGtZjdf7JunXvrL0i+nUSlKuaM2ziNmAvXuXPjVqb180BalnMdWri2o3rDmvzafyoAJioVtW3f5QfHsSTG3WXo76No49aRo1v880TbK4VmIvPEOj9OrPOjcc/WdP62F1vG/Jn3WiR5Qm4H58FAMyFEKeApEIJmkN4O2A3UB45pg4EZcEJbr5MQYhyawXJ5IALwzGIbO/S21Vv72Q7oKYRw1y4XA9JHiH6KoiRqPwcCK7UXDLsURQnLyZdTFGUpsBSgePFqmR6JsbEJWOtlaK2sLIiLu5XBJh5ra0tiYxNQqVSULl2KxP9j77zjojjeP/7eOw4VBVRAqootxo411th7w2g0lthj1Bi7JvbEFo01ib33HruAiCiKXQQ7iggivQhiB+7298fhwQkoyvkV85v363Uvbnee2f0wM7v7zLMzc6mv6+3tbdi5cxUDB44mKCjkfeS9k9fnTa/t9TCa7GjLChcXD1xctH2hAQN6oH7PB0d4eCR2DmmRVTs7GyLf0BUeHom9gy0R4VE6XfGPEqhWowrtO7Zk2vRxmJuboZE1vHyVxNpVW9i6eQ9bN2uH6EyaOprw8Ejel/DwSOzt0+rTzt4mw/Ci8LBI7B1sCA9PLTPzAsQ/SqB6jSp06NiK32aM12rTaHj58hW2dta0btOU5i0akidvHkxNC7Bi9XwG/zD2zdO/Xdsbbc3O3paIN7WFa2102jKpz7t3Ann27Dnly5fF1/e67v+LjYnj8EF37STN93DOw8IicLBPfw3YEBHx5jUQiYO9LeGp7cw8E1137gTy/Nlzylcoi++V69k+/9sID4/CLt2bLFs760zbmp19WlszNTMlPj6B8PAozp25pNN5/NgpKlcp///OOY8Kj8barohu29q2CDGRsW/JoU/+AiYs3bKAf+au4tqVmwbUFYNNuohvEVsrot9T15It81kydxXXDagLQB0dg7JIWpkprKxQx8bp2ciJibrvzw8dwXRI2uTzp5u28nTTVgAKTpuM+mGowbRp719v3OPevCZSbd68x9WoWYWOzq34Pd097tWrJFav3JxjXfGRcRS2SzeJ0rYwCdGPMtiVr1eZdsM6M6fbFFKS0gIwCdHxAMQ8jML//E2KVyhhMOc8PvIRhe3SIuWFstBWrl4l2g77hnndpum0JUTG8fBWELEPtWXs536JklXLwIePcMySx5GPME+n09zWgsTUcsmMq4fO0Wnmf+ONoFitJR2yLCcDwUA/4CxwGmgMlAKC0DrKTqmf8rIsD5AkKS+wDOgiy3IlYDVa5zorXqX+VZPWiZCAzumOXUyW5dupac/S6TsFfA2EAZslSer9If/n27h8+SqlS5egePGiqFQqvv22PUeO6E8+OnLEg549OwPwzTdt8PI6C4C5uRl7965n6tQ/OXfO8Ks+vNbm6Jim7fBhfW2HDx+jV68uOm0nT55953GtrLQXf8GC5gwa9D3r129/L12+PtcpWdKRYsUdUKlUdOrcFjeX43o2bi6efNddO1u/g3MrTntp+3XtW/WgWqUmVKvUhJXLN7J4/grWrtoCgKWldoyfvYMt7Tq0YO+ew++lC+CKz3VKlkrT9k3ntrgd0dfm6nKc73po+4kdnVtx2kvrrLVt2QOnio1xqtiYFcs2sGjBCtas2sKM3xZQ8csGOFVszMC+Izl96vx7O+YAPj7XKFnKkeKp2jp3aYfLEQ89G5cjx+me2tacO7XGK7XcXg9DAiha1I4yX5TkQUgoJib5KFAgPwAmJvlo0rQ+t2/dfW9dpUqn6erSpX1GXS4e9Oyl1dUpS132lPmiJCEPDOeI+F25TslSxSlW3B6VSoVz5za4u57Qs3F3PUHX7tqJsO06tuRMqvN98rg35SqUJV++vCiVSurUq5mjCb2fKzf9blO8ZFHsi9lipDKilXMzTrqfzlZeI5URi9fP5dBuV44dMmyk8KbfbYqVdNDT5eXunW1di9bPSdV14t0Z3pNkf3+URe1R2tqAkRH5mjXh1Rn9e6vCorDue576dUl5kBqcUSiQzMy0OkuVxKhUSV5dyn5n+V1c8blGqVLF0+5xXdrimuH+e5zuPbX3346dWnEq9R7XpkV3qlRoRJUKjVi+bAML5y83iGMOEHT1HkUcbbF0KIJSZUSt9vXxPab/XCxWoQR9Zv/I3wPn8CQurXNjYpYfI2Ote1CgkCllqn9JeIDh7iPBb2ir2b4eV9/QVrSCI71mD2LJwLl62oKuBmJinp8ChbV1+mXdigbVlp7Qq4FYOtpQyMEKpUpJlfZ1uHXMR8/G0jEtWPFlk6rEBb9/EEvwvyMn65yfQjvcpD/aoSwL0Ua5zwNLJUkqLcvyPUmSTAAH4HUXPTZ1DHoXYE/qvidAdmY9HUU7Fv1nWZZlSZKqyrLs+6aRJEnFgTBZlldLkpQfqAZsSmeS3fNliVqtZtSoqRw6tAmlUsnGjbu4fTuAKVNGc+XKNY4c8WDDhp2sW7eIGze8iI9P4PvvhwEweHAfSpVy5Ndff+bXX38GoH3774mJiWPWrAl069YRE5N83Lt3nvXrdzBr1uL31jZy5BQOHdqcqm0nt2/fZerU0fj4XOfIkWOp2hZz8+YpHj1KoHfvYbr8d+6cwdTUFGNjFe3bt6Rdu174+wewYMFvVKpUHoDZsxdz717Qe+v6ddx0du9bi0KpZNvmPdzxv8evk4bjd+UGbq6ebN20m2Wr5nHR7xgJ8Y/5od+odx53/ZYlFC5ckOTkFMaP+Z3HCYnvzJOZtvFjf2fP/nUoFUq2bt6Dv/89Jkwaga/vddxcPNmyaTcrVs/nsp8H8fEJDMyGNkOgVqsZN+Y39h3YiFKpYPOm3fjfDmDS5JFcuXIdV5fjbNq4k1VrFuJ3zZP4+Mf06zMcgDp1azBq9GCSU1LQaDSMHjmVR3HxODoWZeuOFYB2GMfuXQffexUGtVrNmNHT2H9wk07X7dsBTJ4yiitXruNyxIONG3ayZu0irl4/QXz8Y/r2/jlVV03GjEnTNWrkFOLitJGe9Rv+osHXtbGwKMSdgLPMmrmYTe85oVatVjNx3Ey2/7sGpVLB9i17ueN/j/ETf8bP9wburifYtnkPS1bO5dwVNxLiH/Nj/zEAPH6cyMqlG3Dz3I0syxw/dgoPdy8Apvw+lk5d2pLPJB9XbmqPMX/O0vfS9j6MmzaHS77XSEhIpKlzL4YO+J7O7Vt+tPOlR61WM3viApZvX4xSqWD/9sME3gli6PgfuOV3m5Pu3lRwKsfidXMwK2hKw+b1GTJuIN807EnLDk2pVtsJ80JmdOimXSpwyoiZ3LmZ88mEarWaPyYuZPn2RSiUynS6BnLTzx+vVF2L1v2h0zV03AC+adgrU11TR8wyiC6tOA2JC/+m8MI/QaHgxRFXUoKCKTCgH8n+d3h15iz5u3yjnSSqVqNJTCRhVuoKI0ZKLJb+BYD8/DkJ02eBAYe1qNVqxo/5nX/3r0epVLJ1s/Y+MmHyCPyu3MDV5TibN+5ixZoF+Fw9Tnx8AgP6jjTY+bNCo9awdeoaxmyagkKp4PQuT8IDHuI86juCr9/Dz+MyXSf0Jo9JXoYu016jcWGx/P3DHOxKO9Bn9o9oZBmFJHFk+T7C7xnOAdaoNWybupaRmyYhKRWc2XWC8IBQOozqxoPrgVz1uEyXCd+T1yQvg9NpW/rDXGSNht2zNjNm61SQJEJu3Of0juPvOOOH6zwwdQMDN01AoVRwaddJogJCaTGqC6HXg7jl4UPdPi0oXa8SmpQUXjx+xs7/yJCW/+ovhEofOt5OkqSmgBtQMHVs+V1ghSzLCyVJagLMBfKkmk+WZfmgJEkzge/QRt0fAg9kWf5NkqTOwGzgBdqx47eBGrIsx0qSVAOYL8tyo9TJoouBumij6MGyLLeTJKlvqv2wVG19gHFAMvAU6C3LcpAkScHpjrsNqAy4yrKsvyZeOrIa1vKpyc0zrQsYv+2FyKdFk0vLLUVjuMmQhia3lhnk7rb28N6RTy0hU6pX7PmpJWRJpnMncglupfO82+gTUN7v4aeWkCXOllU+tYQsMco1i9XpUyiX/zbkn8Hbc9VFesHum4/6gPoqfO8n+X8/2Dn//4Jwzt+f3Oww5VZHUzjnH0ZubmvCOX9/hHP+/gjn/MMQzvmHkduc8/Mf2Tmv/Ymc89zdCgQCgUAgEAgEgkz4rw5ryZ1dR4FAIBAIBAKB4P8hInIuEAgEAoFAIPjsEEspCgQCgUAgEAgEgo+KiJwLBAKBQCAQCD47DLfYaO5CRM4FAoFAIBAIBIJcgoicCwQCgUAgEAg+O+RcvPxqThCRc4FAIBAIBAKBIJcgIucCgUAgEAgEgs8OTe79jbwcISLnAoFAIBAIBAJBLkFEzt+BU+GSn1pCprQzsvvUErJkUfylTy0hS5oWLv+pJWSKa8y1Ty0hS16mJH1qCVmST2X8qSVkSfWKPT+1hEzxubH1U0vIGnXyp1aQJbWq9P/UEjKlonnxTy0hSybke/apJWRJbl0f27LM808t4bNCI8acCwQCgUAgEAgEgo+JiJwLBAKBQCAQCD47xGotAoFAIBAIBAKB4KMiIucCgUAgEAgEgs8O8QuhAoFAIBAIBAKB4KMiIucCgUAgEAgEgs8OMeZcIBAIBAKBQCAQfFRE5FwgEAgEAoFA8NkhxpwLBAKBQCAQCASCj4qInAsEAoFAIBAIPjv+q5Fz4ZwLBAKBQCAQCD47xIRQwVup3agmO05tZLf3Fr7/qXuGdKevKrPBbSWnH3jQuO3XemneIR5sdF/NRvfV/Ll+psG1lWxYmcGe8xjitYA6Q9pnSK/Wsyk/HJ3DQJfZ9N4zFcsy9gDYVSnJQJfZ2o/rbMq2rJFjLU2aNeC8jxsX/Y4xfNSgDOnGxirWrF/MRb9jHPXcTdFi9nrp9g62BIf78tPP/XX7Bv/UF+8LRzh9/jCr1i0kTx7jHOt0aliVvzyX8Y/XCpyHdM6Q3m5gBxZ5LGG+219M3TYdS3srABzLl2DWvrksPPYP893+om67+jnWAtCs+ddc8TvO1esnGD1mcIZ0Y2NjNm76h6vXT3DCax/FUsutcZP6nD5zkAsXXTl95iANG9bR5encuS3nL7hy6fJRZsz89a3nb9miETdvnML/ljfjx/2U6fm3bV2O/y1vznofonhxB13aL+OH4X/Lm5s3TtGiecN3HnPokL743/ImJSkMC4tCuv1jRg/m8iV3Ll9yx8/3OK9ehFCwkHmWmps0bcC5y25c9HVn+KgfMtGsYvX6RVz0dcft+C5dWytazJ6QyKucOL2fE6f3M2/R77o8O/9dwwnvA5w+f5h5i35Hocj5LbRe49oc9N7B4XO76T/s+wzp1Ws7sdN9A1dCT9O8XWPd/rIVyrD58Cr2em1lj+dmWnZsmmMt78Pk2Qv5uu13OPfK2B4/Nt4XfGjXcwituw9izZY9GdLDI6MZMHIynfr+TN/hE4mMjtWlLVy+Aec+w3DuMwzX46cNrq1u46/Y572dA+d20m9Yrwzp1WpXYZv7Oi6FetGsXSPdflsHa7YeXcsOjw3s8dpCl97OBtdWq1FNtp7awHbvTfT86bsM6VW+qsRatxWceOBOozeeU0XsirBg21w2n1zH5hPrsHGwNpguk/rVcXRZg6PbOgoN7Joh3cy5OSXP7KDY3qUU27sUsy6t9NIV+U0oeXILRSYPNZim9NpKuK6mxNG1FP7h24zaOjWj1NkdFN+3hOL7lmDepWVGbV6bKTJliMG1qarVouDyzRRcuZW8XXpkamNcvzHmSzdivnQDBcZO0WqyssZ80SrM/1qD+dIN5GnVweDaBDkj10fOJUkqCPSQZXnZp9aSFQqFgjGzRjCi+ziiI2JY57KC0+5nCQ54oLOJDItixqi59BzcLUP+Vy+T6NMio/NgCCSFRKsZfdnW8w8SIx/R/+AMAjyuEBsQprO5ceAsV7YeB6BMs2o0m9yTHX3+JPpOKGvbT0ZWayhQpCADXWdz1+MKsvrDXiQpFArmLphGl479CA+L5NjJf3FzOc7dO4E6m569vyUh4TG1nJrTqXNbpv0+joH9RurSZ/4xkePHTum2bWyt+eHH76lXqw0vX75izYbFdOrclh3b9n2Qxtc6B8z4kRk9p/EoMo4/Ds7nssdFQgMe6myCbgbxS7vRJL1MokWvVnw/oS+Lhs3j1YtX/DNqMZHBERQqUpi5Rxbgd8qX54nPcqRn4aLpdGj3PWFhkZw6fQCXIx74+9/T2fTp25WEhMdUqdSYLl3aMWPmr/Tp/TNxcY/4tstAIiOiKV/+C/Yf3MgXpetQuHBBZs6eQIN6HYiNfcTKVfNp1KguJ0+ezfT8f/81i1ZtuhMaGsH5cy4cOuzO7dsBOpv+/boTH/+YL8vXp2vXDvwxexI9eg6hXLkydO3akcpOTbCzs+ao6w7KVWgAkOUxz567xBEXD44f03e6FixcwYKFKwBo17Y5I4b/QEL84yzLbM6CqXzr3I/wsCjcT+zBzcUzk7aWSK2qLXDu3Iapv4/lh36jAAgOCqFxg4zO0YC+I3j6RFuX6zf/TYdOrdj/r0u26jErnRP/GMOgriOIiohmu9s6Trqf5v7dYJ1NRFgkk0fMoO/Qnnp5X754yaSfpxMSFIqVtSU73Ndz9sQFniQ+/WA974Nzm+b06NyBiTPm/0/O9xq1Ws3MRStZvXA6NlYWdBs0hsb1a1HKsZjOZv6ydXRo2ZiOrZtywecqi1dtYs7k0Xidu8StgED2rP2LpORk+g6fSIPa1SmQ38Qg2hQKBb/+MYYhXUcSFRHNVrc1eLl7v1GfUUwbMYveQ/WDODFRcfRtP5jkpGTymeRjj9dmvI56ExMViyFQKBSMnjWcUd3HExMRw2qXZZxxP6f3nIoKi2b2qD/5bnBGJ3TyX7+w6e9tXD7tQz6TvGg0skF0oVBQZMpPhA2YSHJULMV3/c2zE+dJCgzRM3vqeoromZm7ARbDe/P80nXD6HlDm/XUnwjtn6pt91889byQQdsTVy+iZyzP9BCWI77nxUfSln/wSBKnjEETF4P5wpUkXziD+mFafSps7cnXpSeJ439CfvYUybwgAJr4OB6P+wlSkiFvPgouWU/SxTPIj+IMr/Mjo/lvBs4/i8h5QcDw3WEDUr7ql4QGhxMeEkFKcgoeBzz5umU9PZvI0CgCb99Ho/nfjpCycyrFo+AoEh7GoElWc+vQeb5oXl3PJunpC913lUke3feUl0k6R1yZR4Wcw3txtRqVCbr/gAfBD0lOTmbfv0do3baZnk3rtk3ZsV3rWB/c70aDRnXSpTXjQfBD7qRzSgGMjIzImy8vSqUSE5N8REZG50hnaacyRAZHEv0wipTkFM4cOk2N5rX0bG6eu07SyyQA7vreobCtBQARQeFEBkcAEB/9iMexjzErbJYjPTVqVOF+4AOCU8ttz55DtG3XXM+mbdvmbN3yLwD79rnSqFFdAK5dvUVkhLY8bt26S548eTA2NsaxRDHuBQQRG/sIgBMnztDRWT8a9ZpaNasSGBhMUFAIycnJ7Np1gA7t9aNDHdq3YPPm3QD8++8RmjSun7q/Jbt2HSApKYng4IcEBgZTq2bVtx7Tz+8mDx6EvrVMunXryI6d+7NMr1a9MsH3H/AgOJTk5GT27z1C67b6keXWbZqwM7UTd2j/URqke6uQFa8dcyMjI1QqFTm9KCpWLU9IUChhIeGkJKfgtt+Dxi31I5bhDyMJuB2Y4d7x4P5DQoK05RQTFcuj2HgKWRTMkZ73oYZTJczNTP9n53vN9dsBFLO3paidDSqVitZNG+DpfUHPJjD4IV9VrwJArWqVOZGaHhj8kJpVKmJkpMQkX17KlnLE+8IVg2mrWLUcD9PV59H9x2nUsoGeTYSuPvXbTkpyCslJyQAY51EhSYb1OspV/ZKw4DAiUp9Txw+coH7Luno2r59T8hvaHMsUR2mk5PJpHwBePH/Jq5evDKIrb+WyJIdEkBwaCckpJLp4kb/Ju6/F1+QpXxqlZUGenzFcPaZp+4LkkHCdticuXhRoWjv72iqURmlRiGcfQZtRmXKoI8LQREVASgqvTnmi+kr/TW3elu156bIP+Zm2wy4/TtAmpKRoHXNAUqnAAG8ABYblc6iROUApSZL8JEmaJ0nSOEmSLkmSdE2SpN8BJElylCTJX5KkNZIk3ZAkaaskSc0kSTojSVKAJEm1Uu1+kyRpsyRJnqn7DRKutrKxJDo8zSGMjojBysYy2/mN8xizzmUFqw8tzeDU5xRTm8I8iUjrDSdGPMLUplAGu+q9mzP01EKaTujO0WkbdfvtnEox6NhcBh2dg9ukdR8cNQewtbUmPDRStx0eHomtnXUGm7BQrXOrVqtJTHxC4cKFMDHJx/BRPzBvzhI9+8iIKJb+sxa/mye5GXCGxMQnnPQ888EaAQrbWBAXkRatehQRh4WNRZb2Tbs1x/ekT4b9pauUwcjYiKgHkZnkyj52djaEhkXotsPCIrGzs3nDxlpno1areZz4RG9ICICzc2uuXb1JUlIS9wOD+aJsKYoVs0epVNK+fXPsHewyP7+9DQ9Dw3XboWERGc+fzkatVvP4cSIWFoWws8skr71Nto6ZFfny5aVli0bs3Zd1xNrWzpqwsHRtLSwKW1v9tmZja01YWMa2BlCsuAOep/dx4MhmatfR78zu2ruG24Fnefr0GQf3H82W5qywtrUiKt29IyoimiK2Vu99nIpVy6NSqXgYHPZu48+c6Ng4bIqk3V+trSyJjtGP+JUtXYJjXtq3QB6nzvHs+QsSHidStlQJTl/w4cXLV8QnJHLJ9zqR0TEG01Ykk/q0eo/6tLYrwk7Pjbj67GPD0q0Gi5rD6+dU2v8aExGDZTafU0VLOvA08RkzV//G2qMrGDp5kEGGdAEYFbEgJTJNV0pULCrrjPfbAi3qU3z/cmwXT8LotW5JwuqXQcTOW2MQLRm0WVuSHJFOW2QsRploM21eH8cDy7D7S19bkV9+IOYjaVNYWKKJTWtrmrgYlBb69am0d0BpVxSzuUswm7cMVbW0IJPC0grzv9dRaP1uXuzZ9llGzQE0SB/186n4HJzzX4FAWZadgGNAGaAW4ARUlyTpdZipNPAXUBn4EugB1AfGAhPTHa8y0BaoA0yVJClzj+Q9yCzCIb9HRK1TrW70bzOYaT/NZOTvw7AvnmNJbyUzbT6bjrHs69F4ztlB/Z/TXueH+wWyqvkvrOswhbpDO6DMo/rg82annDK1QeaXicNZsXQDz54910szL2hG6zZNqV6pCRW/qI+JiQnfdjP8+Lms6rNBp4aUrFSagyv1h9EULFKInxeNYtnYv9+rLWTGB5dbOpty5cowfeYvDP95EgAJCYmMHDGFjZuX4O6xiwcPwlCnpBj4/Fnnzck1065dC86eu0x8fEKWNjkps6jIaKpWaEyTBp2YMmkOK9YsoIBpfp1N128GUvGL+uTJY0yDhtmPomUh9J0634VlEQtm/zOVqSNn5ritfQ5k9j++WZdjh/bjst8NugwYwWW/m1hbWaBUKqlXqyoNateg19DxjJs+jyoVvkR+4KknAAAgAElEQVSpVBpOXGbR7veok6jwaLo16UPHOt1o37U1hS0zBlI+XFsm+7KpTWmkpHKtiiydsZJBbYZiW8yW1l1bvjtjtnS9+xp4evI8QU378MB5CM/P+WLzx1gACnZvx7NTF0mJNFwn5p28UWRPT1zgftO+BHccyrOzvtjMGaPV1qMdz7wufTxtWdxz9VAqUdo5kDhxBE/nTyf/z+OQ8hcAQBMbw+Ph/Ykf1IO8TVshFTRgWxPkmFw/5vwNWqR+fFO3C6B11kOAIFmWrwNIknQTOC7LsixJ0nXAMd0xDsiy/AJ4IUnSCbSOvt77cUmSBgGDAEqYf4F1/rc7y9ERMRSxK6LbLmJrRWxU9nuhr23DQyK4cs6PLyqWJuxB+DtyZY8nkY8wtU3r6ZvZFuZpVNZOzc2D52g1sx+wUm9/3L1wkl68osgXDkRcD/ogLeHhkdg5pEVH7exsdEMu0tvYO9gSER6FUqnEzMyU+EcJVKtRhfYdWzJt+jjMzc3QyBpevkoiJjqWBw9CiYuLB+DwIXdqflWV3TsPfpBGgEeRcVjYpkUgCtta8CjqUQa7SvWq8M2wb5nWdRIpSWmObb4C+Ziwfgrb528hwPfuB+t4TVhYBA72trpte3sbIiKi3rCJxMHelvCwSJRKJeZmpjx6pK1nO3sbtu1YyaCBYwgKShsr6epyHFcX7VyDfv27o1arMz9/aARF00XVHextM54/1SYsLEJ7fnMzHj2KJywsk7zh2rzvOmZWdOva4a1DWgDCwyKxt0/X1uytMwx3igiPxN7+jbaW6vAnJWn/XvO7SXBQCKVKl+Cq7w1d3levknBz8aR1m6Z4ncg4Tj+7RIVHY53u3mFtW4SY93iY5y9gwtItC/hn7iquXbn5wTo+J6ytLPUmeEbFxGJlWVjPpoilBX/N0sZknj9/gceps5gW0HawfuzdlR97aycdjp8+n+JZvDH6EKJzWJ+viYmKJfBOENVqV8Hj8EmDaIuJiKWIXVoU3+o9nlPRETEE3LhHRIj2TZP30TOUr1aeIztcc6wrJSoWI5s0XUbWlqRE699vNQlPdN8f73bDcswAAPI6lSNf9YoU7N4ehUleUBmhef6C2IXrc6zrtTZVujcfRjaWpETrl9mb2qzGahcryOdUjnzVK1CwRzskk7xIKhWaZy8Npk0TG4PCMq2tKSys0DyKzWCTcucWqNVooiLRhD1EYeeAOsBfZyM/iiMlJBhV+coknfUyiLb/Jf/VcMTnEDlPjwT8IcuyU+qntCzLa1PT0g+A06Tb1qDfCXmzLjPUrSzLq2RZriHLco13OeYAt/38KVrCHtuiNhipjGjWsQmn3bP3wDY1L4DKWBuNNi9kRuWaFQm6++AdubJP+NX7FC5hg3lRKxQqJeXb1+buMf0hGIUc0173l2niRHywdjiAeVErJKW2iZjZW2JR0paE0A9/Bezrc52SJR0pVtwBlUpFp85tcUt1Dl/j5uLJd907AdDBuRWnvc4B0L5VD6pVakK1Sk1YuXwji+evYO2qLYSGhlOjphP58uUF4OuGdbh75/4HawS4dzUA2xK2FClaBCOVEfXaN+DysYt6No4VSjDojyHMHTCLxLi0SYlGKiPGrZqA178nOO/y4U5benx8rlGqtCPFU8utS5f2uBzx0LNxcfGgZy/tqjKdOrXGK7XczM1N+fffdfw29U/On9evdysrbaetYEEzfhjUi40bdmZ6/kuX/ShdugSOjkVRqVR07dqRQ4fd9WwOHXbn+++1k8g6d27LiZNndPu7du2oHefuWJTSpUtw8ZJvto6ZGWZmpnzdoDYHD759OInvleuUKJXW1py/aYubi6eejZuLJ916aNtae+eWeJ86D4CFRSHdK/vijg6ULOXIg+CH5M9vgrW19kGtVCpp1qIhAXdz1tZu+t2meMmi2BezxUhlRCvnZpx0z94KIkYqIxavn8uh3a4cO+T57gz/ESp+WYaQ0HBCwyNJTk7G9fhpGtf7Ss8mPiFRN0Z/9dY9dGqjnduiVqtJeJwIwJ3AIO4GBlO3ZlWDabvp50+xkg7YpdZnS+emnHT3zlbeIrZW5MmrXWnK1NwUp5qVCL4X8o5c2cffzx+HdM+pph0b453N55S/3x1MC5pSsLB2daRq9aoSbKDn1Mvrd1AVt8PI3hpURpi1acizE+f1bJRWaZ2vAk1qk3RfWy6R4/8kqGlvgpr1IebPNTw5cNxgzq9W211Uxe1QpWozbdOQp55vakuLOBdoUpukQO3CARHj/uR+kz7cb9qXmD/XkHjAw6DaUgL8Udo5oLC2ASMj8nzdhOSL+kM6k857Y1RJ274lM3MUdkXRRIajsLACY21bk/IXQFWuIuqwhxnOIfh0fA6R8yfA61lHR4EZkiRtlWX5qSRJ9kDyex6voyRJfwD5gUZoh83kCLVaw4LJf7N4258oFAoO73Ql6G4wP4ztx+2rd/A+dpZyVcoyZ+0MTM0LUL95HQaO6UfPJv1wLFOcX+aMRiPLKCSJzUu2682ezymyWsPRqRvovukXFEoFV3d5ERsQxtejOxNxLYgAjyvU6NOCEvUroklW8yLxGQdHa1fEKFqjLHWHtkeTrEaWNbhNXs+L+A9fCUKtVvPruOns3rcWhVLJts17uON/j18nDcfvyg3cXD3Zumk3y1bN46LfMRLiH+tWz8iKK5evcejAUTxP7yclJYXr126zaf2OD9YIoFFrWDt1FZM2/YZCqeDEruOEBjyk2+geBF67x2WPi3w/sR95TfIxZtl4AGLDY5k7cBZ12tWjXK0KmBY0pXGXJgAsHfs3wbc+7G0DaMttzOhp7D+4CaVSweZNu7l9O4DJU0Zx5cp1XI54sHHDTtasXcTV6yeIj39M394/A/Dj4D6ULFWcXyb8zC8TtPs6tu9NTEwcf86bSqVK5QCY88ff3LuXuUa1Ws2IkZNxObINpULBho07uXXrLr9NG8tln6scPnyMdet3sHHD3/jf8iY+PoEevbRzuG/dusuePYe4fvUEKWo1w0dM0jlNmR0TYNhP/Rk7Zig2Nlb4+njg6ubJj4PHAeDcsTXHPE7x/PmLTLWm1zxh7HR27V2DQqlk+5Z/ueN/j18mDsfP9wZHXT3ZunmPtq35uhMf/5hB/bVtrU69mvwycTgpKWo0GjVjR00jIf4xVlYWbN6xHGNjY5RKBd6nzrNhXc7amlqtZvbEBSzfvhilUsH+7YcJvBPE0PE/cMvvNifdvangVI7F6+ZgVtCUhs3rM2TcQL5p2JOWHZpSrbYT5oXM6NCtDQBTRszkzs2Ad5zVMIybNodLvtdISEikqXMvhg74ns7tDTTU4S0YGSmZOPJHfhz7G2qNhk5tmlG6RDGWrN1KhbKlaVz/Ky75XWfxyk1IkkT1KhWYPEq73GNKiprewyYAUCB/PuZMHo2RkeGGtajVauZOXMSy7QtRKJUc2H6Y+3eCGDJ+ILf8/PFy96a805csXPcHZgVN+bp5PQaPG0iXhr0oUcaR0b8Nez0ejE3Lt3PPP2edP31tGhZN/ocF2+aiUCg4stOV4LsPGDC2L/5X73Dm2Dm+rFKWWWt/x9S8AHWb16H/mD70bjIAjUbD0ukrWbxzPkhw93oAh7YdMZAwDTEzl+GwZhYoFCTudSfp3gMsfv6elzcCeHbiPIV6dSR/k9qQokb9+AmRExYY5tzZ0BY9YzkOa2eCQsnjf91JuheSqu0uz05coND3HSnQuDayWo3mf6lNo+bZisWY/T4fFApeebigDgkmX8/+pAT4k3zxLMlXLqKqWhPzpRtBo+H5+uXITxIxcqqBaf+haGOTEi/27UT9wHBt7X/Jf/VHiKTPYYyiJEnb0I4VdwVCgYGpSU+BXoAaOCzLcsVU+w2p23skSXJ8nSZJ0m+AHVAKKAb8Kcvy6redu45941xZQO2MPu649JywKP7Sp5aQJY0LlfvUEjLFNebap5aQJS9Tkj61hCwplK/Ap5aQJbb5Cr/b6BPgc2Prp5aQNer3jbX876hVpf+7jT4BBZR5P7WELFlr9uFzlD42spw71+CzLPP83UafEItDXrmq4Pba9PioPto3kds+yf/7OUTOkWX5zdX1/8rErGI6+77pvgenTwPuyrKc8ddvBAKBQCAQCASfDRoDLzmaW/jcxpwLBAKBQCAQCAT/WT6LyLmhkGX5t0+tQSAQCAQCgUCQc3LluGMDICLnAoFAIBAIBAJBLuH/VeRcIBAIBAKBQPDf4L+6WouInAsEAoFAIBAIBLkEETkXCAQCgUAgEHx2aP6bi7WIyLlAIBAIBAKBQJBbEJFzgUAgEAgEAsFnh4b/ZuhcRM4FAoFAIBAIBIIPQJKkVpIk3ZEk6Z4kSb++xa6LJEmyJEk13nVM4ZwLBAKBQCAQCD475I/8eReSJCmBpUBroDzQXZKk8pnYmQLDgQvZ+b+Ecy4QCAQCgUAg+OzQSB/3kw1qAfdkWb4vy3ISsAPomIndDOBP4GV2DirGnL8DRS4dz6TMpboANHLu/c0uo1xabgopd+oCcmmJ5X6k3Fpy6uRPrSBrlKpPrSBLjBTKTy0hU0wUxp9awlvIvc8CScqd2iQRMs1VSJI0CBiUbtcqWZZXpdu2Bx6m2w4FvnrjGFWBorIsH5YkaWx2ziucc4FAIBAIBALBZ8fH/hGiVEd81VtMMovC6Hp+kiQpgEVA3/c5r+ijCQQCgUAgEAgE708oUDTdtgMQnm7bFKgInJQkKRioDRx816RQETkXCAQCgUAgEHx25ILBSZeAMpIklQDCgO+AHq8TZVl+DFi+3pYk6SQwVpbly287qIicCwQCgUAgEAgE74ksyynAMOAocBvYJcvyTUmSpkuS1OFDjysi5wKBQCAQCASCz45srqjyUZFl2QVweWPf1CxsG2XnmCJyLhAIBAKBQCAQ5BJE5FwgEAgEAoFA8NnxsVdr+VSIyLlAIBAIBAKBQJBLEJFzgUAgEAgEAsFnh4icCwQCgUAgEAgEgo+KiJwLBAKBQCAQCD475FywWsvHQETODcRXjWqy/dRGdnpvptdP3TOkV/mqMuvcVuL14BiN2n6tl3Yq5Bgb3FexwX0Vc9fPNLi2Eg0r84PnPH70WkDtIe0zpDv1bEL/o3/Qz2UWPfdMwaKMnV66mZ0Fo2+todagNjnW0rRZAy5cOcplPw9GjB6UId3Y2Ji1GxZz2c+DY557KFrMXi/d3sGWkAg/hg0fkKbP3JQNm//hvI8b5y+7UbOWU451VmlYlQWeS1nktZwOQ77JkN5mYAfmefzDXLfFTNo2HUt7KwAs7a2YdXgBf7gsYt6xv2nWs2WOtQA0a/41Pr4e+F3zZNSYwRnSjY2NWb/xb/yueeJ5ci/FUsutevXKeJ87jPe5w5w5f4R27VsAkCePMSe89nHm/BEuXHJj4qSR2dbSokUjbtw4xe1b3owb91OmWrZuXc7tW96c8T5E8eIOurTx44dx+5Y3N26connzhgB88UUpLl9y133iYv0Z/vNAAKZMGU1w0GVdWqtWTbKts0nTBpy77MZFX3eGj/ohE50qVq9fxEVfd9yO79K1taLF7AmJvMqJ0/s5cXo/8xb9DkC+fHnZtmslZy+5cvr8Yab8NibbWt5G3cZfccB7O4fO7aL/sO8zpFer7cQO9/X4hJ6iWbvGuv1lK5Rh0+FV7PXawm7PTbTs2NQgetLjfcGHdj2H0Lr7INZs2ZMhPTwymgEjJ9Op78/0HT6RyOhYXdrC5Rtw7jMM5z7DcD1+2uDa3sbk2Qv5uu13OPfKeK18bOo0qsWe01vYe2YbfYb1zJBe9asqbD66hnMhnjRp2zBDev4CJhzx+Zdxs7J/TWaXGo2qs+bkatafXkvXod9mSK/4VUWWuPyDS9Bh6repr5c2YGJ/VnmsYLXnSob8bthyNalfHUeXNTi6raPQwK4Z0s2cm1PyzA6K7V1Ksb1LMevSSi9dkd+Ekie3UGTyUIPqyu3aVFVrYb5sM+YrtpK3c49MbYzrNcZ8yUbM/tlA/tFTtJqsrDFbsAqzRWsw+2cDeVp98HLcgo9Ero+cS5I0UZbl2Z9ax9tQKBSMmTWCkd3HER0RwxqX5Xi7nyU44IHOJiosilmj5tJ9cMaL+9XLJPq2yOioGgJJIdFiRh929JzDk8hH9D04nQAPH+IC0n5d9taBc/ht9QSgdLNqNJ3ci119/tSlN53ak/snr+ZYi0Kh4M8Fv/FNx76Eh0Vy3Otf3I54cufOPZ1Nr95dSEhIpIZTM77p3Jbfpo9jQN+0h9TsOZM4fuyU3nH/+HMyxz1O0ff7n1GpVOQzyZsjnZJCQb8ZPzK75zTiIuOYdXAePh4XCQsI1dkE37zPpHZjSHqZRLNeregxoQ9/D5tPfHQ80775hZSkFPKY5GWe+9/4HLtIfHT8B+tRKBQsWPg7Hdv3JiwskpOn9+NyxIM7/mnl1rtPVxISEnGq3ITOXdrx+4xf6NdnOLdu3aVh/Y6o1Wqsbaw4e/4Iri7HefUqiXZtevLs2XOMjIxw99jFMfeTXLrk904tf/81i9ZtuhMaGsH5cy4cPuzO7dsBOpv+/bqTEP+YcuXr07VrB2bPnkTPnkMoV64M3bp2pIpTE+zsrHFz3UH5Cg24ezeQGjVb6I7/INiH/Qdcdcf76+/VLFq08r3LbM6CqXzr3I/wsCjcT+zBzcWTu3cCdTY9e39LQkIitaq2wLlzG6b+PpYf+o0CIDgohMYNnDMcd+k/6zhz+gIqlYq9BzfQtNnXHPc4lcHufXRO/GMsP3YdQVRENNvc1nLS/TT37wbrbCLDIpkyYiZ9huo/fF++eMnkn6cTEhSKlbUl293XcfbEBZ4kPv1gPelRq9XMXLSS1QunY2NlQbdBY2hcvxalHIvpbOYvW0eHlo3p2LopF3yusnjVJuZMHo3XuUvcCghkz9q/SEpOpu/wiTSoXZ0C+U0Mou1dOLdpTo/OHZg4Y/7/5HyvUSgUjJ89imHfjSYqIoaNLqs4ddSboHTPgsiwKH4fOZteg7/L9BiDxw/kyvm3X4cfqu2nmT8xocdEYiNi+efwX5w/doGQgBCdTUxYNAtGL6DLj5318pavXo4KNcozuIXWwVywdz6Va1fi2vnrhhBGkSk/ETZgIslRsRTf9TfPTpwnKTBEz+yp6ymiZy7L9BAWw3vz/JIBtHxm2kx+HMmTaWPQxMVgNn8lSRfPoHmY1tYUtvbk7dKTxF9+Qn72FMm8IACa+DgSf/kJUpIhbz7M/15P0sUzyI/iDK/zIyPGnH86Jn5qAe+iXNUvCQ0OIzwkgpTkFI4f8KRBy7p6NpGhUQTevo+s+d82JVunUsQHR/H4YQyaZDW3Dp2nTPPqejZJT1/ovqtM8iCn+0HcMi2qkxASQ+zdsBxrqV6jMkH3H/Ag+CHJycns/fcIrdvpR/vatG3Gjm17ATiw342vG9VJS2vXjODgh/incwRNTQtQt25NNm/cDUBycjKJj5/kSGdppzJEBkcQ/TAKdXIK5w55U6P5V3o2t87dIOllEgD3fO9Q2NYCAHVyCilJKQCojFVIipy/c6tRowr37z8gOLXc/t1zmLbtmuvZtG3XjO1b/wVg/z5XGjXStr8XL16iVqsByJsnD3K63zp+9uy5VqfKCCOVEbL87h9CrlWzKoGBwQQFhZCcnMzOXQdo317/7UD79i3YvFlbH//+e4Qmjeun7m/Jzl0HSEpKIjj4IYGBwdSqWVUvb5Mm9bl//wEhITlrb9WqVyb4/gMeBIeSnJzM/r1HaN1Wv621btOEndv2AXBo/1EaNKyT2aF0vHjxkjOnLwDadnbt6i1s7a1zpLNi1fI8DAolLCSclOQU3PZ70KhlAz2b8IeRBNwORPPGvePB/YeEBGk7jDFRsTyKjaeQRcEc6UnP9dsBFLO3paidDSqVitZNG+DpfUHPJjD4IV9VrwJArWqVOZGaHhj8kJpVKmJkpMQkX17KlnLE+8IVg2l7FzWcKmFuZvo/O99rKlQtx8PgMMJSnwXHDhynYUv9CHREaCT3bt9H1mS83r6s9AWFrQpxweuSwbWVdfqC8OBwIkMiSUlO4eRBL+q0qK1nExUaTZB/MJo37gWyLGOcxxgjYyNUxiqMVEriYxMMoitv5bIkh0SQHBoJySkkuniRv8nbr8X05ClfGqVlQZ6fMXz7ys3ajMqUQxMZhiYqAlJSSDrtiXEt/baWp0V7XrnsQ36m7bDLj1PrLCVF65gDkkoFis/BFfz/Ra6qEUmS9kuS5CNJ0k1JkgZJkjQHyCdJkp8kSVtTbXpJknQxdd9KSZKUqfufSpI0NzW/hyRJtSRJOilJ0v3XP6EqSVJfSZIOSJLkJknSHUmSphlCt5WNJdHh0brt6IhYrGyssp3fOI8xa12Ws+rQEhq0rGcISTpMbQrxJOKRbvtJxCNMbQplsKvWuxk/nlpA4wnf4TFtEwCqfHmoPaQd3ov3GkSLra0NYWERuu3wsEhsbfWdG1s7a8JCIwFt5C7x8VMKWxTCxCQfI0YN4s8//tGzL+5YlNjYRyxZMZeT3gf4a8ksTEzy5UhnIZvCxEWkvZ6Pi4ijkE3hLO0bdWvG1ZNpN9/CtpbMdVvMkvNrOLhib46i5gC2djaEhqYvtwjsMim31zZqtZrExCcUttDWc40aVbhwyY1zF10ZOXyyzllXKBR4nztMYPAlTnie4fLld78dsbO3ITQ07a1LWFgE9nY2GWweptqo1WoeP07EwqIQ9nYZ89rZ6+ft1rUjO3fu19s3dEg/rvgcY/WqBRQsaP5Oja/LIywsUrcdHhaVoa3Z2Frr2qOuzApry6xYcQc8T+/jwJHN1K6j35kF7VCqFq0bc9rrXLb0ZEURWysiw6N029ERMVjbZv/e8ZqKVcuhUql4GJzzTrROS2wcNkUsddvWVpZEx+hH1sqWLsExr7MAeJw6x7PnL0h4nEjZUiU4fcGHFy9fEZ+QyCXf60RGxxhMW27FysaSqHTPgqiIGKyyWZ+SJDFy2k/8PWP5R9FmYWNJTHhaHcRGxGJpY5GtvLev+HP13DW2X97Kdp+t+Hhd4eG9hwbRZVTEgpTINF0pUbGorDPqKtCiPsX3L8d28SSMbFLbpSRh9csgYuetMYiWz0mbZGGJOjatrWniYlBYWOrZKO0cUNgVxXTOEsz+XIaqai1dmsLSCrO/1lFw7W5e7t32WUbNQRs5/5ifT0Wucs6B/rIsVwdqAMOBecALWZadZFnuKUlSOaAbUE+WZSdADbwe1JcfOJma/wkwE2gOdAKmpztHrdQ8TsC3kiTVeFNEasfgsiRJlyOfhb+ZnAFJyhgdzU4U8jWda33HgDZD+O2nWYz4/Sfsi9u9O1O2ySRym4m0K5s8WPn1GE7O2UHdn7Wv8+uP/oZLa9xIfv7KMEoyk/JGOWVVlr9OGs7yJet10d7XGBkpqeJUgfVrttGofkeeP3vByNE/5kxnNssMoH6nhpSsVJpDK/fp9j2KiOWXViMZ9fVgvu7cGHPL7DmUWerJTrllqllrc/nyVb6q2YpGXzszZuwQ8uQxBkCj0VC/TjvKfVGX6tUrU678F9nQ8u62nrnNu/OqVCratWvBnn8P6/atXLmJsl/WpXqNFkRERjPvz0x/EdmAOmWiIqOpWqExTRp0YsqkOaxYs4ACpvl1NkqlklVrF7JmxWYeBIdmOMb7kJ26fReWRSyY9c9Upo6c9d5530Zmx3qzzMYO7cdlvxt0GTCCy343sbayQKlUUq9WVRrUrkGvoeMZN30eVSp8iVKpNJi23EpOngVd+nbijOd5PefekGTe1rKX187RlqKli9Kz1vf0qNmLKnWrUPGrih9N2Jtl9vTkeYKa9uGB8xCen/PF5o+xABTs3o5npy6SEhmb4Rj/eW3ZeU4plSjtHHgyaQRP508n/7BxSPkLAKCJjSFxRH8SBvcgT+NWSOYZg3aCT0duG3M+XJKkTqnfiwJl3khvClQHLqXeBPMBr+9kSYBb6vfrwCtZlpMlSboOOKY7xjFZluMAJEnaC9QHLqc/iSzLq4BVAPXsm7zz9hUdEUMRuyK67SK2lsRGZf+CjI3S9ljDQyLwPedHmYqlCXvw7k5BdngS+QhT27Sor6ltYZ5EZR3JvXXwPC1m9gPAzqk0X7auReMJ35HHzARZlkl5lcyVjcc+SEt4eCT29ra6bTt7GyIj9R9E4WGR2DvYEB4eiVKpxMy8APGPEqheowodOrbitxnjMTc3Q6PR8PLlKw7udyM8LBKf1KjvgQNuOXbOH0XGYWGbFoGwsLUgPupRBruK9SrjPKwL07tO1g1lSU98dDyhdx9StlZ5Lrp8eIQ1PCwSB4f05WZLxJvlFq610ZWbmSmPHum/dr57J5Bnz55TvnxZfH3TxkA+fvwE79MXaNb8a27fuvtWLWGhETg4pHUe7e1tCY+IymBT1MGOsLAIlEol5uZmPHoUT2hYxrwR6aLGrVo1xtf3OtHpJhWm/7527Vb279/4Vn268giLxD5dVN7O3jpDW4tIbY8R4VG6MouP15ZZUpL27zW/mwQHhVCqdAmu+t4AYOFfM7gfGMzK5dnT8jaiwmOwsUuL6BextSL6PR7m+QuYsGTLfJbMXcX1KzdzrCc91laWehM8o2JisbLUf4NUxNKCv2ZpRx4+f/4Cj1NnMS2g7cj82LsrP/bWzrEZP30+xR0MGXTInURHxGCd7llgbWtFbDbrs3L1Cjh9VZkufZwxyZ8PI5WKF89esGT2+823yIrYiFis7NKi+Ja2lsRFZS9aWrdlXfx9/Xn5/CUAl09cplzVL7lx4UaOdaVExWKU7k2zkbUlKdH691tNQtpQxce73bAco10QIK9TOfJVr0jB7u1RmOQFlRGa5y+IXbg+x7pyuzY5LgalZVpbU1hYoXmk39Y0cTGk3LkFajWa6EjUYQ9R2DqgvuefdpxHcagfBmNUoTLJZ70Mou1/ieHCER3fTGsAACAASURBVLmLXBM5lySpEdAMqCPLchXAF3hzZp8EbEyNpDvJslxWluXfUtOS5bQurQZ4BSDLsgb9TsibdZnjuvX388ehhD22RW0wUhnRtGMTvN2z54yZmhdAZawCwLyQGZVqViT47oN35Mo+EVfvU7iEDeZFrVColJRvX5t7x/THvxVyTHMOSjdxIj5YOxxg67czWF5/FMvrj+LyuqOcW3rwgx1zgCs+1ylZypFixR1QqVR807ktbkeO69m4uhznux7a1VE6OrfitNd5ANq27IFTxcY4VWzMimUbWLRgBWtWbSE6OpawsAhKlykBQMOGdfQmSn4IgVcDsClhi1XRIihVRtRpXx+fYxf1bBwrlGDgH0OZP2A2iXGPdfsL21igSo1M5zfLT9kaXxIRmLOOlo/PNUqWcqR4arl17tIOlyMeejYuR47Tvad2Epdzp9Z4pQ63KF7cQRexLFrUjjJflORBSCgWloUxN9eOyc2bNw+NGtcj4M79d2q5dNmP0qVL4OhYFJVKRbeuHTl82F3P5vBhd77/XrsSROfObTlx8oxuf7euHTE2NsbRsSilS5fg4iVfXb5u3ZwzDGmxsUl7+Dh3bM3Nm3feXWCA75XrlEjX1py/aYubi6eejZuLJ916aGMB7Z1b4n1K29YsLAqhSB2DWdzRgZKlHHkQrH2FP2HySMzMCzDpV8PMUb/pd5tiJR2wL2aLkcqIVs7N8HL3zlZeI5URi9bP4dBuV44dOmEQPemp+GUZQkLDCQ2PJDk5Gdfjp2lcT3/uRXxCom4s/Oqte+jUphmgHSaU8DgRgDuBQdwNDKbuG/ML/ovc8vOnWAkH7Ipq67N5x6accj+TrbxThs2gfc1v6fhVN/6avgyXPUcN5pgD3Ll6F3tHO6yLWmOkMqJRh4acP3Y+W3ljwmOo/FUlFEoFSiMllWpXIsRAw1peXr+DqrgdRvbWoDLCrE1Dnp3Q16W0SusUFmhSm6T72gmZkeP/JKhpb4Ka9SHmzzU8OXDcYM5vbteWEuCPwtYBRREbMDLCuEETki/qt7Xk896oKmmvO8nUHIV9UTRR4UgWVmCsfU5J+Qtg9GVFNGGGqU+BYchNkXNzIF6W5eeSJH0JvJ6pkixJkkqW5WTgOHBAkqRFsixHS5JUGDCVZfl9vNnmqfleAM5A/5wKV6s1LJr8Dwu3zUWpUHJ4pytBd4MZOLYv/lfv4n3sLF9WKcsfa6djal6Aes3rMHBMX3o16U/xMsUZP2cUGllGIUlsWbJdb5WXnCKrNbhP3Ui3TeORlAqu7fIiNiCMBqM7E3EtiHseV6jepwXF61dAk6zmZeIzjow23AMhPWq1mvFjf2fP/nUoFUq2bt6Dv/89Jkwaga/vddxcPNmyaTcrVs/nsp8H8fEJDExdPeNt/DJ2BivXLMDYWEVw8EOGDfk1Rzo1ag0bpq5mwqZpKJRKTu7yIDTgIV1Gdyfo2j18PC7RY2Jf8prkZcSy8QDEhccwf+Bs7Es70GtyP2RZRpIkDq86wMM7OatPtVrNuDG/se/ARpRKBZs37cb/dgCTJo/kypXruLocZ9PGnaxasxC/a57Exz+mX5/hANSpW4NRoweTnJKCRqNh9MipPIqLp0LFL1mxah5KpRKFQmLfvy64uXm+Q4lWy4iRkzlyZBtKhYING3dy69Zdpk0bi4/PVQ4fPsa69TvYsOFvbt/yJj4+gZ69tCs83Lp1l917DnHt6glS1GqGj5ikc+zy5ctLs6ZfM3ToL3rnm/PHZKpUKY8sywQ/CM2Q/jadE8ZOZ9feNSiUSrZv+Zc7/vf4ZeJw/HxvcNTVk62b97Bs1Twu+roTH/+YQf21ba1OvZr8MnE4KSlqNBo1Y0dNIyH+MbZ21oweN4S7dwLxPKUdxrR29Ra2bMq4xGB2UavV/DFxIcu3L0KhVLJ/+2EC7wQxdPxAbvr583/s3XdUFFcfxvHv3QWsWMAGdkWNFexdrFhRY01ij0ZjiUZj773F3nvvMTYECyqKvYudoqCUpYMmsQDLvH+AyAoIwhIw7/2cwznszG9mHmaHmbt3785eOH2JilblWbplHrnyGGPdogFDxvSnk3VPWrZvRrU6VuTOm4v23WNuczp1xBxcH7kns9WUMTBQM/HXQQwaPR1tdDTftmmORclirNq8m4rlLGjSoDY37z1g2fodCCGoblmRySNjbrEXFaWl97AJAOTMkY35k0dhYPDvDWsZM20+N+/eJzz8Nc069mRI/150ttXPbU0/R6vVsnDSMlbsWYRareLYPgeeu3kxaMyPPHFxxfn0ZSpYfsPCzbPJlceYBi3qMWj0j3Rv0ifds0Vro1k9ZS1zd81GpVZzev9pXri9pPdvvXC778Y1x+uUtSzL1I1TMM6dkzrNa9N7VE8GNv+Zi/aXsKxnyXrHtSgK3Lpwi+tnrie/0ZTQRhM0ew1FNs0BlYrXh04T4fEC01968e6hO/84XSNvzw7kaFoHorRoX/2F/4TF+tn215wtWsubDcswnr4IVCren3VA6+1Fth9+JMrjKZE3rhB59waGVWuSe9V2FG00b7etRfnrNQaWNcj+45AP4w15d2Q/2hfJd85kRtH/0fucC32OUUwLIUQW4AhQGHAF8gPTgdZAe+BO7Ljz7sAEYnr9I4GhiqJcE0L8rShKzth1TQf+VhRlUezjvxVFySmE6Au0IWZ8ugWwR1GUGZ/LlZJhLRnB1iDzvkW8MFRPJ+100NKkYkZHSJR98P2MjpCkt5H6+cxBesiTLWdGR0iSebaUfdju33bLRX+9d3qnNszoBEmqV6VvRkdIlIk6R/JFGWRlzkx5+czU8pV9m3xRBjI5eiFTNYeXFuuZrgfZyJe7MuTvzTQ954qivCemIf6p88C4eHX7gf2JLJ8z3u/Tk5oHBCqKMiyNcSVJkiRJkiRJ7zJN41ySJEmSJEmSUuq/+iVE/1eNc0VRtgHbMjiGJEmSJEmSJCXq/6pxLkmSJEmSJP03/Fc/1ZBpbqUoSZIkSZIkSf/vZM+5JEmSJEmS9NX5r95KUfacS5IkSZIkSVImIXvOJUmSJEmSpK/Of/VuLbLnXJIkSZIkSZIyCdlzLkmSJEmSJH115N1aJEmSJEmSJElKV7LnPBm3QzwyOkKi8hXIntERkmSW3SSjIyQpKpO+zo7QRmV0hCTlyZYzoyMk6U3k+4yOkKSTlbNkdIRE1bL8MaMjJMlApc7oCEm6cn9bRkdI1MAaYzI6QpKaB7hldIQkGaoMMzpCotTBmbvPNLM9o9GZ9JqeVpn7KJAkSZIkSZKk/yOy51ySJEmSJEn66si7tUiSJEmSJEmSlK5kz7kkSZIkSZL01flvjjiXPeeSJEmSJEmSlGnInnNJkiRJkiTpqyPHnEuSJEmSJEmSlK5kz7kkSZIkSZL01YkWGZ0gfcjGuSRJkiRJkvTVkV9CJEmSJEmSJElSupI955IkSZIkSdJX57/Zby57ziVJkiRJkiQp05CN8zRo0cKa+/edePTImdGjhySYb2RkxM6dq3n0yBln56MUL14EABOTPJw6tY/g4CcsXTpTZxlDQ0NWr57PgwfncXE5R8eOrdOcs6p1NdY4rWOd8wY6D+mSYH77AR1ZdXYNy0+tZObeOeQvnD9u3rQdM9j9YB+Tt05Nc45PNWhSh+OXD3Di2kEG/NI7wfzqdaz4w3E7Lr6XsWnXVGfe+r3LuOp2htW7Fus9F4CVdVWWn1vDygvr6Di4c4L57Qa0Z+mZVSw6uZype2aSL3aflahQkjmHF7DEcSWLTi6nXrsGesmTWY+1ps0acvXWSW7cPc3wkT8lksuQjVuXcuPuaU6ePUDRYoUBKFqsMC/9XXC6eASni0f4fekMALJly8qeA+u5cvMEF68dZ8r037440wctWlhz995Z7j84z2+/DU4kmxHbd6zi/oPznL9whGLFYvZZ06YNuHTZjhs3TnLpsh3W1nUTLHvgj43cvHkq1dniy1K7Jvn3bCf/vl3k6Pl9gvnZWrekgN1h8m3dSL6tG8nWrk3cPOPBA8m3Ywv5dmwha9MmeskTX70mtTl8aS9Hr+6n37CeCeZXq2PJntNbuOlzgebtGsdNNytSkN2nNrPvzDYOXthFl94d9ZqrbuNaHLy4i0OX99BnWI8E86vWtmTnqU1cfXmOpm2tE8zPkTM79rf/ZMycX/WaKzmT5y6hUdvv6Njz5391ux9UsrZi7tkVzD+/ijaDv00w36a/LbMdlzHzxBLG7J6GabxrweZnB5jhsIgZDosYvnF8mrNYN6uP0/VjON+yZ8iI/gnmGxkZsnrz7zjfsueo426KFDUHoGOXtpy48Efcj1ewCxUqlSNHzuw60++5OzNt7thUZWvUtB6O1w5x7sZRBg3vm2i2FZvmc+7GUf48tZ3CRc0AMDQ0YMGK6Tg47+f4+X3Url89bpnfJg7lkosD970upSpTcho2rcvJq3/ieOMwA4f3STC/Rt2qHD67i8eaa7S0bZYuGTJCdDr/ZJSvbliLEKIEcFxRlEoZmUOlUrF8+Wzatu2Bj4+Gy5ftOH7ckadP3eNq+vbtTnj4KypWbETXrrbMnj2BXr2G8u7de2bMWEyFCuWoWLGsznrHj/+FoKBgKldujBACE5M8ac45aPZgpvWYTIgmhEV2S7nheB1vd++4Gs9HzxjVdiQR797Tqmdr+k7sx+9DFwJweP0hsmTLQsserdKUI7Fck+aP4aduvxDgF8j+U9twOnWRZ26ecTUa3wAmjZhF38EJL75b1uwiW7asdO2d8AKjj2z9Zw1iVo9phPqHMO/YIm6duYGPzj7zZFy7UUS8i8CmZyt6TejL0mG/8/7te1aOXIa/l4a8BUxYYL+Ye853efP6nzTlyYzHmkqlYv7iqXTt2A8/3wBOOx3kpMM53FyfxdX06N2V8PDX1KpqQ8fObZg6YzQ/9RsJgJfnS5o0TNhoW71yC5cvXsfQ0JBDx7bRrHkjzp5x/uJsS5bOxLZdT3x9/bl48Rj29o48feoRV9OnbzfCw19RpXJjunSxZdbs8fTpPYyQkDC6dOmPvyaQChXKcvTYDspY1Ilbrn2Hlvzz95svyvOZoOQaNYLQkWPQBgaRb9M63l+6QpTXC52yd+eceL10hc60LHXrYFi2DMH9BiAMjTBZtYz3166jvNFPNpVKxfh5vzG4268EaALZfXITF05f4rmbV1yNxjeAaSPm0HuI7ouKoIAQ+tr+TGREJNmyZ+PghZ1cOHWJoIBgveQaO3ckw74bRYAmiO0OG3A+dQlP94/7zN83gBm/zqXnz98luo6fxw7gzrV7ac7ypTq2acEPndszcdaif33bQqWi18yfWNRzJqH+IUw9toB7jjfx8/CJq3n52JOZtmOJeBdBk54t6TahF2uHLQEg4l0E09qM1ksWlUrF7IWT6NFpIBo/f+zO7sPxpBPurs/jarr37MSr8Nc0qtEW206tmDB9JEP7j+HIQXuOHLQHoFz5MmzevYLHD10BaG3dNW55+3P7OWF3NlXZpi8YR58uQ/D3C+Cw4y7OnryAR7xrU9ceHXkV/pqmtTrQ7lsbxk0bwfAB4+neqxMAbRp1xzRfXrbsX0XH5j1RFIWzp5zZsXk/Z68fSdU+Sy7ztPnj6Nd1KP5+Afx5egdnTzrrXk99/Bn/y3T6D+ml9+1L+id7zlOpZk0rnj3zwtPzJZGRkfzxhx22tjY6Nba2NuzadRCAQ4ccaNKkPgBv3rzlypWbvH//LsF6+/TpxsKFqwFQFIWQkLA05SxjVRZ/Lw0BLwOIioziop0ztWzq6NQ8uPqAiHfvAXC964qpWb64efcvu/D277dpypCYytUq4O3pg88LPyIjo3A44kiTVo10avy8Nbg99kCJTvj69frFW/prIH3CwqoM/l7+BHrH7LPLdhep0aKWTs2jqw+IeBcBgNtdV0zMTAHQePrh76UBICwwlFfBr8hlkitNeTLrsVatehW8nr/ghZcPkZGRHDlkT+u2uj0yrds0Zf+ewwDYHTlFw0R6oeN7+/Ydly9eByAyMpL7Lo8xK1zwi3IB1KhhxfNnL/Dy8iYyMpKDB+1o1053n7Vra8PuXX8CcPiwA40b1wPAxeUR/ppAAB4/diNLliwYGRkBkCNHdn75ZQALFqz84kyJMSz/DVofP7R+GoiK4u2Zc2RpUD9FyxqUKE7EPRfQRqO8e0eUxzOy1KmV/IIpVKlqebw9ffB96UdUZBSnjpylccuGOjUab3/cnzwjOlp35GdUZBSREZEAGGUxRAj93e+sYtXyeHv54vtSQ1RkFI5Hz2LdUvcdKo2PPx5PnqNEJxyR+k3lspjkz8v1Czf1limlalhVJncu4399uwClrCwIfOFPkHcA2sgobthdoqpNTZ2ap1cfxp3Xnt11I28h03TJYlW9Ml6eL3n5wofIyCjsDp3AprXuOz82bZpwcN8xAByOOlK/Ue0E6+nQuTVH/3RIML1EqWKY5jfhxtXbX5zNslolXnj64P3Cl8jIKI4fPkXz1o11apq3bsyhfccBOHHsLHUbxuxHi3KluHLxBgAhwWG8fvUXla0qAHDv9gO9vDhNTJVqFXnh5R2X2f7IaZq31n3HyNdbg+tjD6KV/9bX9kSjpOtPRvlaG+dqIcRGIcQjIcRpIUQ2IcR5IUQNACFEPiGEV+zvfYUQR4QQdkIITyHEMCHEKCHEXSHENSGESWoCmJsXwsfHL+6xr68Gc/OCSdZotVpev/4LU9O8Sa4zd+6YRty0aaO5etWe3bvXUqBAviTrU8K0kCnBfkFxj0M0wZgWTPqE26K7DbedvvyE9qUKFiqAxi8g7nGAXyAFC+X/zBL/HpNCpoRoPp5EQzUhmH7mItWsewvunk+4zywsy2BgZEDAC/805cmsx5qZeUF8fT/+bX6+AZiZ6eYqZFYQX1+NTi4Tk5hcxYoX4dzFwxy130mdutX5VK7cxti0bsLFC1e/KBeAuXlBfHx195lZgn32sSapfdaxY2vuuzwiIiKmwTJ16m+sWLGJN28SvthJDXX+fGgDA+MeRwcFoc6f8HnIat2IfNs2kWfWdFQFYv5PIj2ekaV2bciSBZE7F0bVrFAX0N//UAGz/AT4fcwWoAkkv1nK11/QvAD7z23nxO3DbFu9W28Nk/yF8n2SKyjFuYQQ/DptKCtmrdVLlq9J3oImhPrFP6+Fkvcz14JG3Zrx4PyduMeGWYyYemwBkw/Po6pN2l4EFjIrgF+8c4fGL4CCCc4dH2u0Wi1/vf6bvJ+8u2f7bSuOHjqRYP0dOrfB7vDJVGUraJYfjd/HbP5+gRQ0K/BJtvxoEsn29JEbzVtZo1arKVLMnEqW5VPVufDlmQvg7/vxeppYZunr8rU2zssAqxVFqQiEAwkHBeuqBPwA1ALmAG8URakKXAUSDHYWQgwUQtwSQtzSav9OdIWJ9QQpivLFNfEZGKgpUsScq1dvUbduW65fv838+ZM/82elQCIdVkllsP62MRZVLDi8/s+0bTMlEsuViT93ndQ+a/itNaUqW3Bs/WGd6XkK5OWXpSNZM3rFZ5/zlMisx1pacgX4B1K1YhOaNvyWKZPms27TYnIa54irUavVbNi8hE3rdvLCyyfBOvSRjWRqypcvw6zZ4/nll4kAVKlSgVKli2N3TD9jzZPKwCc5312+SmDX7wnuO4CIW7fJMylmvG/EzVu8v3aNfOtWkXf6FCIfPkbR6rFXLAXZPifAL5DuTfvQoW53bLu1xiRf0i8WvyzWlx3r8XXp+y2Xz13Tadz/3/iC/Va3YyNKVCnNiQ1H46aNrjeIme3HsX74Mn6Y2o/8xVLf6NTHOc2qemXevn2H2xOPBHXtO7Xi2J8JG+2pzZbguE8i2x+7j+KvCeTImV1MnjOaOzdc0Gq1qcrxJRKPnHmvp/qkpPNPRvlaG+eeiqJ8GDB4GyiRTL2Toih/KYoSBLwC7GKnP0hsWUVRNiiKUkNRlBpqdc5EV+jrq6FIEfO4x4ULm6HRBCZZo1aryZXLmNDQ8CRDhoSE8c8/bzh6NOYV/6FD9lhZpW1ofYgmhHzmH3uVTM3yERoYmqDOsoElXYd1Z07/WURFRKVpmykRoAnU6cksaF6AQP/0ecvvS4X6h+gM7TExMyU0IOE+q1zfkk7DurJgwBydfZYtZzYmbJ3C3kW7cL/rluY8mfVY8/P1p3DhQnGPzQsXxN9fN5fGz5/Chc10coWFhRMREUlYWEy++/ce4eX5ktIWJeOWW7J8Fs+febF+7fYvyvSBr68/RQrr7jP/T/aZX7yaT/eZeeFC7N23np8GjMLT8yUAtWpXo2rVyjx+cokzZ//AokxJTpzcl6p8H2gDg1AX+NjDpcqfH21wiE6N8vo1RMYMEXljZ49huY+fHfh7x26C+/1E6MgxIARa7y9/IZOUQL9ACpp/zFbQrABBqfgfDQoI5pmrJ9XqWOonlybok1z5CU5hrirVK9KtXyeOXt/PiKlDaNOlJcMmDtJLrswuzD8EE/P45zUTwhO5FlSoX4V2wzqzfMA8nfNaeGDMsLcg7wCeXntE8YolEyybUhq/AMzjnTvMzAsSmODc8bFGrVZjnCsn4WGv4ua375T4kJbyFcuiVqt54PI4Vdn8/QIxM/+YrZB5AQL8gxLWJJJNq9UyZ/JibJt8z8+9RpErtzFez16mKseXZi4Ur4e+kHkBAj/JLH1dvtbG+ft4v2uJ+WBrFB//nqyfqY+O9ziaVH4o9tYtFywsSlKiRFEMDQ3p2tWW48cddWqOH3ekZ8+Yu6N06tSG8+evJLtee/szcXeHaNKkPk+euCezxOe5u7hhVtKcAkULYmBoQEPbRtxwvK5TU7JiKQbPG8ac/rN4FfIqiTXp18O7TyhWqiiFi5lhaGhAm44tcDr1ZR/6Sy8eLu6YlTSjQNECGBgaUN+2Ibccb+jUlKhYkoHzBrOg/xxex9tnBoYGjNkwgQt/OnHNIfnnOyUy67F2984DSpYuQbHiRTA0NKRjp7acdDinU3PS4Rzdf4j50K5tx5Zccr4GgKlpXlSqmH/X4iWKUKp0CV54xXzgdsLkX8mVOyeTxs/9ojzx3b7tQmmLEhSPzdaliy329rr7zN7BkR49Y950+/bbNly4ELPPcufOxaE/tzJt6kKuXfs4XGnTxl1YlK5NhfINaN6sKx7unrRulfgHDlMq8ulT1EULozYrBAYGZGvelPeXdZ87lenHkXdZGtQj6kXsxV6lQuSKGZ5kULoUBqVL8f6m/sZRP7r3lGKlimBezAwDQwNadmzG+dMpu9NEAbP8ZMkaM07fOLcxVjUr4+Whn0bK43tPKVayCOZFY3K16NAM59OXU7TslGGzsK3ZlQ61u7N85hocDp5i1dz1esmV2Xm6eFCghBn5ihRAbWhALdsG3HW8pVNTrGJJ+swdxIoB8/kr5HXc9Oy5cmBgFHOpzJnXmDLVv8HPPfUvBF3uPKRkqeIULVYYQ0MDbDu1xvHkeZ0axxPn6fJdewDadGgRN5YbYnq323awwe5QwqErHTq34VgiQ11S6v7dR5QoVZQixcwxNDSg3bctOXvygk7N2ZMX6PRdOwBat2/G1Ysx/3dZs2UlW/aY5kd969pEabU6HyRNLw/uPqZEyY+Z23a04ezJzHE9TW/ybi2ZnxdQHbgBJLxfoJ5ptVp+/XUKdnY7UavVbN++nydP3Jg6dRS3bz/A3t6Rbdv2s2XLMh49ciY0NJzevYfFLe/qehljY2OMjAyxtW1Ju3Y9efrUncmT57FlyzJ+/30awcGhDByY+lvJAURro9kwZR3Td85EpVZxdr8j3m4v+WFUDzweuHPD8Qb9Jv1ItuxZGbs25u3yYL8g5vSfBcDcgwsoUroIWXNkZfP1bawas4K7znc+t8kU0Wq1zJmwiA37VqBSqzi8145nrp4MGzuQRy5PcDp1kUpW5Vm+dSG58hjT2KYhQ8f8RAfrmLtC7Di6npIWxcmeIxtn79oxdeRsLp+/nsxWUyZaG83mqRuYtGM6KrUKpwNn8XH3pvuoH3h234NbZ27Qa2I/smbPxm9rYm7VFewXzIIBc6jbrj7la1XEOI8xTbrE3P5x9egVeD1O/Qk6sx5rWq2WCaNncuDQJlRqNXt3/YnrUw/GTRzOvbsPOXXiHLt3HmTNht+5cfc0YWGvGPhjzJ1a6tavybiJw4mK0hIdrWX0yGmEh73CzLwgo8YMxs31GeecY4YKbd64i107Dn5xtt9GTeXosR2o1Wp27DjAkyfuTJ4ykjt3HuBgf4bt2w6wafMS7j84T1hYOH16/wLAoJ97U6p0ccZPGM74CcMBaG/bi6CgkM9tMnW00bxesgKTJQtBpeKt/QmiPL3I2b8fkU9deX/5Cjm6dIr5kKhWS/Tr14TPmR+zrIEa09XLAVDevCF85hzQ47AWrVbLgolLWbN3CSq1mqN7j/Pc1ZPBYwfw+N5TLpy+RAWrb1iyZR658hjTqEV9fh4zgC7WPSlZpgSjpg+LGQ4gBDvW7sXj6fPkN5rCXAsnLWPFnkWo1SqO7XPguZsXg8b8yBMXV5xPX6aC5Tcs3DybXHmMadCiHoNG/0j3JglvL/dvGzNtPjfv3ic8/DXNOvZkSP9edLZt+a9sO1obze6pm/htxxRUahUXD5zDz92bjiO/w+uBB/fO3KLbhN5kyZ6VIWtizgUhvsGs+Gk+5hZF6DN3ENGKgkoI7Nce1rnLy5fSarVMGTuXnQfXoVar2b/7MG5PnzFqwlAe3H2E48nz7N91iGXr5uF8y57wsFcMG/Dxtoi161VH4+fPyxcJM7Tr2JI+3RPebvZLss0Yv4Btf6xGpVJxcM8x3F2f8+v4n3lw7zFnTzpzYPcRFq+ZxbkbRwkPf8WInyYAYJovL9v+WE10tEKAJpDfBk+JW++4aSOw7dyKbNmzcun+CQ7sOsKKhfp5YajVapk54Xc2H1iJWqXm4N5jeLg+Z/i4QTy85byrpQAAIABJREFU94Rzp5ypbFWB1dt/J1fuXDSxacjwsQNp27C7XrYv6Z/42sYlfXorRSHEaCAnsA84APwNnAN6KopSQgjRF6ihKMqw2Hqv2MfBn85LTNasxTLlDmpZoEpGR0iSx/vM+3ZahayFki/KAHYBdzM6QpKMs2TL6AhJehP5PvmiDOJRLfVv+6enNh4RGR0hSQYqdUZHSNKV+9syOkKiBtYYk9ERknT2ddqH9aUXQ5VhRkdIlFpk7gENbkG39HfrJT0YVeK7dG2jLfHalyF/71fXc64oihcxH/D88Dj+DWPjt1gnx87fBmyLV18i3u868yRJkiRJkiQpI311jXNJkiRJkiRJypRDG/Qgc79/IkmSJEmSJEn/R2TPuSRJkiRJkvTV+W993+lHsudckiRJkiRJkjIJ2XMuSZIkSZIkfXUy8zeLp4XsOZckSZIkSZKkTEL2nEuSJEmSJElfHTnmXJIkSZIkSZKkdCV7ziVJkiRJkqSvTvR/dMy5bJxLkiRJkiRJX53/ZtNcNs6TVTq3WUZHSFR2kXmfOo9XfhkdIUllsxTI6AiJ0kZrMzpCkl6/f5PREZIkEBkdIUkV7nlndIREVcpdPKMjJCm7yiijIyRpYI0xGR0hURtu/Z7REZLUwmpgRkdIUj519oyOkKisslkmIRvnkiRJkiRJ0lfovzqsRX4gVJIkSZIkSZIyCdlzLkmSJEmSJH115K0UJUmSJEmSJElKV7LnXJIkSZIkSfrqKHLMuSRJkiRJkiRJ6Un2nEuSJEmSJElfHTnmXJIkSZIkSZKkdCV7ziVJkiRJkqSvjhxzLkmSJEmSJElSupI955IkSZIkSdJXR445lz6rQZM6HL98gBPXDjLgl94J5levY8Ufjttx8b2MTbumOvPW713GVbczrN61OF2yWVpXZfG51Sy9sJb2gzslmN9mQHt+P7OSBSeXMWnPTPIVzg9AvsL5mXN8MfMclvK74wqa92iZ5iw2LRrz4P55Hj+6yOjRQxLMNzIyYtfONTx+dJGLzscoXrwIACYmeTh1aj8hwU9ZtnRWXH22bFk5cngb912cuHvnDLNnjU9zRoCq1tVY5bSWNc7r6TSkS4L57Qd0YMXZ1Sw9tYIZe2eTP3afAUzZMZ1dD/YyaevUNGWwsWnMw4fOPHl8iTFjhiaYb2RkxO7da3ny+BKXL9nF7SuAsWOH8eTxJR4+dKZFC+u46blz52Lfvg08eHCB+/fPU6d2dQCmTx/DnduO3Lp5Ggf7PZiZFUxZRj0/n/H9eXALd26fSVGOxLRoYc39+048euScZLadO1fz6JEzzs5HP8m2j+DgJyxdOlNnmS5dbLl58xR37pxhzpyJqc4WX7Pmjbhx5zS3Xc7y66hBiebcvH05t13O4uh0kKLFCuvML1LEDG9/F4YN76+XPPHValyT3c7b2HtpBz2GfpdgvmXtymw+uQ6nF6dp3LaRzrwC5gVYvGcBO89vYafTFgoVSdkxlRI1Gldn0/mNbL24mW5DuiaYX6l2JVY5rMTB8zgN2jTQmdd/4o9sOLOOjefWM3jGz3rLFLdtayvmnl3B/POraDP42wTzbfrbMttxGTNPLGHM7mmYxjt3bH52gBkOi5jhsIjhG/VzLkupyXOX0Kjtd3Tsqf99kpxajWuy48JWdl/azg+JHGdValdmw4m1nPU6hXXbhnHTrepZsunUurif0x4ONGhZT6/ZrKyrsfzcGlZeWE/HwZ0TzG83oANLz6xi8ckVTNszK+76WaJCSeYcXshSx5h59do1SLBsWlWxrsrv51ay+MJqbBM51loPsGXBmeXMPbmECXumxx1rpoXzM+v478xxWMx8x2U07WGj92xS2ui9cS6EcBBC5PmC+hJCiIf6zpHCbf+tj/WoVComzR/Dzz/8SvuG39HmWxtKly2pU6PxDWDSiFnYHzqdYPkta3YxYdh0fURJQKhU9Js1iAV9ZjK6+S/Ua9+QwmWK6NR4PXrOpHa/Ma7Vr1x3uMIPE/oAEBYYxrRO45jQZiSTO4yl/eDO5C2QN9VZVCoVy5fPpn2H3lhaNaV7tw58800ZnZp+fb8jPDycChUbsmLlJubMjmkAvXv3nhkzFjF+/OwE6126bD1VLJtQq3Zr6tarSUubxqnO+CHnwNk/M6vPdIY3G0qD9o0oUqaoTs3zR88Z3XYUI1sO54r9ZXpP7Bc378j6QywbuSTNGVYsn4OtbU+qWDbhu+4dKV9ed1/92O97wsNeUb5CA5av2MjcuZMAKF++DN27dcDSqint2vVg5Yq5qFQx/+pLl8zk9CknKle2pnr1Fjx56g7A4sVrqVa9BTVq2uDgcIbJk0amKGN6PJ8AHTq04u9//vmynZZItg4d+mBl1Yxu3donyNa3b3fCw19RsWIjVq7cxOzZE+JlW8z48XN06k1M8jBv3kRat/6eatWaU7BgPpo0qZ/qjB9y/r5kOl079adOjVZ07tqOct9Y6NT06tOVV+GvqG7ZjLWrtzJ91lid+XMWTOKMo3OaciSVbdSc4YzuOYFeTX6kecemlChTXKcmwDeQuSMXcubI2QTLT14+jr1rD9Cr8Y8MbDuEsOBwveUaOnsok3tP4aemg2jSoTHFyhTTqQnyDWTxqMU4HXHSmV6henkq1qjAzzZDGNR8MGUty1KlTmW95IKY822vmT+xtO8cJrX4ldrtG2BuoXu+ffnYk5m2Y5naehS3Tlyj24RecfMi3kUwrc1oprUZzYqf5ustV0p0bNOCdUsS/39MTyqVihGzf2Fcr4n0adKfph2aUPyT5zPQN5D5oxZy5sg5nen3rrgwoOXPDGj5MyO7j+Hdu3fcvHBbr9kGzBrEnD4zGNk88WuB56PnjGs3it9aDeeqwxV6TegLwPu371k5cikjWwxjdu/p9Js2gOy5cugtm1Cp6DPrJxb2mc3Y5iOo074h5gmu7Z5MaTeGia1GccPhKt9PiOk4DA8MY0anCUxq8xvTOozHdnAn8qTh2p6RohUlXX8yit4b54qitFEURT9n4a9E5WoV8Pb0weeFH5GRUTgccaRJK91eJD9vDW6PPVCiE74Jc/3iLf75+026ZLOwKoO/l4ZA7wC0kVFctbtEjRa1dWoeX31IxLsIADzuumJiZgqANjKKqIgoAAyNDBEqkaYsNWta8eyZF56eL4mMjOTAH8ewtdV9xW5ra8POXQcBOHTIPq7x8+bNW65cucm79+916t++fceFC1cBiIyM5N7dBxQuYpamnGWsyqDx0hDwMoCoyCgu2TlTy0Z3nz28+oCIdzFZ3O66Yhq7zwAeXL7P27/fpilDrZpVdfbV/gNHsbXVfefC1taGnTv/AODPP+1p2qRB7PSW7D9wlIiICLy8vHn2zItaNatibJyTBg1qs2XrXiBmf7169RqAv/76+Do1e47sKCk4KaXH8wmQI0d2Roz4iXnzVqRoX6Uk2x9/2CWabVdcNocE2d6/f6dTX7JkMdzdPQkODgXg3LlLdOzYOtUZAarXsOT58xe88PImMjKSQwftadO2uU5N67bN2bv7MABHD5/EunHduHlt2jXnhac3T5+4pylHYspX/QZfL180LzVERUZx9qhTgl5Jf58Anj15jhKte7yUKFMctYGaWxdjGkpv37zj/buEz3VqlLMqi5+XH/4v/YmKjOL8sQvUtamjUxPgE4jnU68EF1dFUTDKYoSBkQGGRoYYGKr19qIBoJSVBYEv/AmKPd/esLtEVZuaOjVP451vn911I28h08RW9a+rYVWZ3LmM//XtfmNVDl8vv7jj7NzR89S30X3R6+8TwPMnnolePz+wbtuI60439Xacge71Myoyist2F6n5yfXz0dUHcc+n+11XTM3yAaDx9MPfSwNAWGAor4Jfkcskl96ylbayIMBLE3esXbO7RPUWtXRqnuhc292SuLYbpPnaLunfFzfOhRBjhRDDY39fKoQ4F/t7MyHELiGElxAiX2yP+BMhxEYhxCMhxGkhRLbY2upCCBchxFVgaLx1VxRC3BBC3BNC3BdClIldz1MhxPbYaQeFENnjreeCEOK2EOKUEMIsdnppIcTJ2OkXhRDfxE4vKYS4KoS4KYRI/L30VChYqAAav4C4xwF+gRQslP8zS/x78hYyIUQTHPc4RBNC3kImSdY37t4cl/N34h6bmOVjwcllrLq2iWPrDhEWGJbqLObmhfD28Yt77OurobB5oQQ1PrE1Wq2W16//wtQ0Za/oc+fORdu2zXFyupzqjAAmhUwJ9tPdZ6YFk76ANu/egjtO+uutATAv/HE/QBL7qvDH/anVann16jWmpnkpbJ5wWfPChShVqjjBwSFs3rSUmzdOsX7d72TPni2ububMcTx/dpPvv/+W6TN+Tz5jOj2f06eNYdmyjbx9m/oXOOaJ7QPzgknWpCTbs2cvKFu2NMWLF0GtVmNra0ORIuapzghgZl4QXx9N3GM/X3/MEuT8WKPVann96m9MTPOSPXs2RowcxIJ5K9OUISn5C+Uj0C8o7nGQJoh8hfKlaNmipYrw9+t/mL1xOptPrWPI5IFx796klWmhfATFyxWsCSZfChu4T+48xeXqffbe2s3e27u5feEO3h7eeskFkLegCaHxzh2hmlDyfubc0ahbMx7EO98aZjFi6rEFTD48j6o2tZJc7r8kv1k+gjSBcY+D/IPIb/blL1iatm/MuU961tPKpJApwTrXz2BMPnOsNe3egrvnE14LLCzLYGBkQMALf71ly1vIlFBNSNzj0GSu7dbdm31ybTdl7sklLL+2kePrDhOehmt7RlLS+SejpOZs6Qx8GPRVA8gphDAEGgAXP6ktA6xWFKUiEA58GLC1FRiuKErdT+p/BpYrimIVu26f2OnlgA2KolQBXgNDYre5EuiiKEp1YAvw4X3oDcAvsdNHA2tipy8H1iqKUhPQ339JIi86M8vtfUTi4RLV4FtrSlW2wG794bhpoZpgxrX6lZGNfqZR5ybkzpc79VlEwiyf9tAmUpKiXly1Ws3OHatYvXornp4vU50xJkPyOT+w/rYxpatYcGT9oTRtMzUZEq9JelkDtZqqVSuzfv0OatZqyT//vGHs2GFxNVOnLqBU6Zrs3XuYIUP6JVhH6jImXO5zz2eVKhUoXbo4x46dTHb7ac+W8ucZIDz8FcOHT2LnztWcPXuQFy98iIqKSvecie1ERVEYP2kEa1dv5Z9/0uddt8ROHaTwbV61gZoqtSqxetZ6BrYZglkxM1p3S/tnViCpYyply5qXMKOoRVF61OrFDzV7YlnPkkq1K+klV1Lhkjqm6nZsRIkqpTmx4WjctNH1BjGz/TjWD1/GD1P7kb+Y/sbpZ16Jn8e+hEkBE0p9U5IbF27pKVOMxK6fST2fDb9tTOnKFhz95FqQp0Beflk6ktWjV6ToWpbybIlIYvX1v21EqcoW2K8/EjctVBPCxFaj+K3REBp2bkKuNFzbJf1LTeP8NlBdCGEMvAeuEtOQbkjCxrmnoij34i1XQgiRG8ijKMqF2Ok749VfBSYKIcYBxRVF+dB15q0oyofu0F3EvBAoB1QCHIUQ94DJQBEhRE6gHvBH7PT1wIdxDvWBvYlsV4cQYqAQ4pYQ4lbY28CkyuIEaAJ1ersKmhcg0D/4M0v8e0L9Q+LeZgMwNTMlLCA0QV2l+lXoOKwLiwbMjXu7K76wwDB83LwpV6tCqrP4+mooGq+nsXBhM/w0AZ/U+Mf1RqrVanLlMiY0NPm3ndesWYCHhycrV21Odb4PQjTB5DPX3WehgQn3WZUGlnQZ1o15/Wcnus/SwtdHo9Mrm+i+8vm4P9VqNblz5yI0NAwf34TLavwC8PHV4OOj4cbNuwD8ecieqlYJx9vu23eYb79tk3zGdHg+69SuTtWqVXB1vcK5s4coU6Ykp08fSDZLYtkS7ANNYJI1KT3WHBzO0KhRBxo3/hZ39+d4eHh9cbb4/Hz9dYZhmRcuhP8nOePXqNVqcuXOSVhoODVqWjJj1lhcHp1n8JC+jBo9mJ8G9UJfgjTBFDD/+A5gfrP8BAeEfGaJjwI1Qbg/9EDzUoNWG82lU5cpW7lM8gumQLAmmPzxcuUzy0dICnPVa1mPp3ef8u7NO969ecctp1uUr/qNXnIBhPmHYBLv3GFiZkJ4IueOCvWr0G5YZ5YPmKdz7vjQexnkHcDTa48oXrFkgmX/a4I0QeQ3KxD3OH+h/AT7p+z5/KCJrTUXT15GG6XVa7YQ/2Dy6Vw/8yV6/axc35LOw7oyf4DutSBbzmxM3DqVfYt2437XVa/ZQv1D4oapQExPeGLZKtavQvthXVjyybH2QXhgGL5pvLZnpGiUdP3JKF/cOFcUJRLwAvoBV4hpkDcBSgNPPimPP/hLS8ytGwVJvL5TFGUP0B54C5wSQny4rcmn9Urseh4pimIV+1NZURSb2L8pPN50K0VRyn+ybHJ/4wZFUWooilIjb7YCyZXz8O4TipUqSuFiZhgaGtCmYwucTun/A1qp8czFnUIlzchftABqQwPq2jbgtuMNnZoSFUsyYN4QFvWfy+uQV3HTTQqZYpjFCIAcuXJQrsY3aJ75kVq3brlgYVGCEiWKYmhoSLeu7Tl+3FGn5vhxR3r1jLk7SqdObTl/PvkhKtOnjyF3LmN+Gz091dnic3dxx6ykOQWKFsTA0IAGto24+ck+K1mxFIPnDWVu/1m8irfP9OXmrXtYWJSM21fdu3Xg+HHdDxMfP36aXr1i7lTRuXNbnGL31fHjp+nerQNGRkaUKFEUC4uS3Lh5l4CAIHx8/ChbtjQATZs24MkTNwAsLD42Amzb2eDq+izZjOnxfG7YuJOSpWpQrlw9mjbrhLu7JzY23ZLNkni2j/uva1fbRLP1jMvWhvPnryS73vz5Yy6GefLkZuDAXmzdujeZJT7vzu37lC5dnGLFi2BoaEinLm054aD74cqTDmf5vkfMnRg6fNsK5wvXAGhj8z2WFRtjWbExa9dsY8mitWxcn2Sfwxd7eu8pRUoWxqxoIQwMDWjWoQmXTie/j2KWdcU4jzF5TGJ646rVr4qX2wu95HJ1caNwCXMKxv5/Nm5vzTXHaylaNsgviCq1K6NSq1AbqKlcpzIv9TisxdPFgwIlzMhXJOZ8W8u2AXcddXtzi1UsSZ+5g1gxYD5/hbyOm549Vw4MjGLubpwzrzFlqn+Dn7sP/3WuLq4UKVmYQrHHWdMOjbnimLLj7INmHZpy9qh+h7QAeHxyLahv25Cbjtd1akpWLMWgeUOY33+2zvXTwNCAsRsmcuFPJ646pG2oZWKeu3joXNvr2DbgjuNNnZriFUvy47yfWdJ/XpLX9uy5clCmxjdonvnqPaOUeqm9z7kzMcNFfgQeAEuA24qiKIm9TRufoijhQohXQogGiqJcAnp8mCeEKAU8VxRlRezvVYDnQDEhRF1FUa4C3wOXAFcg/4fpscNcyiqK8kgI4SmE6Kooyh8iJlAVRVFcgMvAd8T0vvdAT7RaLXMmLGLDvhWo1CoO77Xjmasnw8YO5JHLE5xOXaSSVXmWb11IrjzGNLZpyNAxP9HB+nsAdhxdT0mL4mTPkY2zd+2YOnI2l89fT2arKROtjWbb1I1M2DENlVrN+QNn8HH3psuo7/G878HtMzf5YWJfsmbPyog1MXeBCPELYtGAuRS2KELPyf1QFAUhBMc3HMXbNfUXWK1Wy6+/TuG43S7UajXbtu/nyRM3pk79jTu373Pc3pGt2/axdcsyHj+6SGhoOL16f7yFoKvrFXIZG2NkZIitbUvatuvBX3/9xYTxw3n61J3r104AsHbdNrZu3ZemfbZxyjqm7ZyBSq3i7P4zeLu95PtRPfB44M5Nxxv0mdSPrNmzMmZtzO3OgvyCmNc/5k4Hcw7Op3DpImTNkZWN17eyeswK7jnf/eJ9NeLXydjb70GtUrFt+34eP3Zj2rTR3L7twvHjjmzZuo9t21bw5PElwsLC6dEz5naBjx+78cdBO+67OBGl1TJ8xCSiYz9I9evIKezYvhIjI0Oee75kwIBRMZnnTKBs2dIo0dG8eOnL0KHJ38YtPZ7Pp0/188HGD9ns7HaiVqvZHpdtFLdvP8De3pFt2/azZcsyHj1yJjQ0nN69Pw7xcXW9jHG8bO3a9eTpU3cWL55O5coxPUxz5y7Dw8MzzTnH/jaDP49sRa1Ws3vnHzx94s6EySO4d+chJxzOsnP7AdZtWsxtl7OEhYXTv++vadpmyrNFs3TyShbvWYBKpcJ+/wm83F7Qf3Rfnrq4ctnxKt9YlmPO5hkY585JvRZ1+fG3PvRu2p/o6GhWz1zPsv2LQIDbA3fs9tjrJVe0NprVU9Yyd9dsVGo1p/ef5oXbS3r/1gu3+25cc7xOWcuyTN04BePcOanTvDa9R/VkYPOfuWh/Cct6lqx3XIuiwK0Lt7h+Rj/n2g/Zdk/dxG87pqBSq7h44Bx+7t50HPkdXg88uHfmFt0m9CZL9qwMWfMbACG+waz4aT7mFkXoM3cQ0YqCSgjs1x7Gz+Pfa5yPmTafm3fvEx7+mmYdezKkfy862+pnKNLnaLXRLJ+ykt93z0elUnFi/0m83F7Qb3QfXF3cuOJ4lXKW5Zi9aTo5c+ekbou69B3Vh37NBgBQqEhB8pvnx+Xqfb1ni9ZGs2nqeibvmI5KreJc7PWz+6gfeHbfg1tnbtBrYl+yZs/Gb2vGARDsF8SCAXOo264B5WtVJGceYxp3ielnXD16OV6P03bOiJ9t+9RNjN0xFZVaxYUDZ/F196bzqO/wvP+MO2du8v3E3mTNnpXha0YDEOIXzJIB8zC3KMIPk/vEDoMEhw1H8XFN23DQjJJZhhDrm0jNGCghRDPgJDHDU/4RQrgB6xRFWSKE8CJ2LDpwXFGUSrHLjAZyKooyXQjxYYz4G+AUMePGKwkhJgA9gUhixoT/AOQCHIh5QVAPcAd6KYryRghhBawAchPzQmOZoigbhRAlgbXEDGcxBPYpijIzdvqe2No/gcmKouT83N9asWDtTPnMV8matjuSpKdDAfr9cKQ+tSlgldEREnXc/07yRRlEXx/kSw+JfqYik8hmYJTRERJVKXfx5IsySHZV5txnAObq7BkdIVEbbiX/4e2M0sJqYEZHSFK+TPp8Zs3k3w2568WhTHXS7V68Y7q20fa/OJIhf2+qjgJFUc4S0+j98LhsvN9LxP4aTMyY8A/TF8X7/TZgGW+V02OnzwPmxd+WECIXEK0oSoJvRogdz94okemeQKskpsf/EOq/eyNZSZIkSZIkSfqMzP0STZIkSZIkSZISkZEf2kxPmb5xriiKF/F64CVJkiRJkiTpvyrTN84lSZIkSZIk6VP/1Q+EZt5PekmSJEmSJEnS/xnZcy5JkiRJkiR9daIzOkA6kT3nkiRJkiRJkpRJyJ5zSZIkSZIk6auTmu/q+RrInnNJkiRJkiRJyiRk41ySJEmSJEn66kSjpOtPSgghWgkhXIUQHkKI8YnMHyWEeCyEuC+EOCuESPZrmmXjXJIkSZIkSZK+kBBCDawGWgMVgO+FEBU+KbsL1FAUpQpwEFiY3HrlmPNkNMlWIqMjJMo3+k1GR0jSD4VqZXSEJN15p8noCImqYFIcv7chGR0jUWqReV/Dn85jkdERkrQUo4yOkKgJ2f7J6AifkXnHjzYPcMvoCIlqYTUwoyMkyfHehoyOkKTdllMzOkKiOrUJzOgIX5VMcLeWWoCHoijPAYQQ+4AOwOMPBYqiOMWrvwb0TG6lmfeqK0n/RzJrw1ySJEmS/l8JIQYKIW7F+/n01XBhwDveY5/YaUnpD5xIbruy51ySJEmSJEn66qT3N4QqirIB+NxbQCKxxRItFKInUAOwTm67snEuSZIkSZIkSV/OByga73ERwO/TIiFEc2ASYK0oyvvkViob55IkSZIkSdJXJ6V3VElHN4EyQoiSgC/wHfBD/AIhRFVgPdBKUZQUfahAjjmXJEmSJEmSpC+kKEoUMAw4BTwBDiiK8kgIMVMI0T627HcgJ/CHEOKeEOJYcuuVPeeSJEmSJEnSVyczfEOooigOgMMn06bG+735l65T9pxLkiRJkiRJUiYhe84lSZIkSZKkr04muM95upCNc0mSJEmSJOmrk963UswocliLJEmSJEmSJGUSsudckiRJkiRJ+upkglsppgvZcy5JkiRJkiRJmYTsOdeT8taWdJnaF5VaxZX953Bce1RnftP+ban7XVOio7T8HfqaXWPXEeYbDECH8T2o1LQqQqXi6cX7HJyxTa/ZqlpXo//0n1CpVZzZ58ihNQd15rcf0IHm39ugjdLyOvQ1q0YvJ8g3CIApO6ZTrmo5ntx6wpx+M/Waq5K1FT9M/RGVWoXz/rM4rD2sM9+mvy2NvmtGdFQ0f4W+YsvYNYTE5tr87AA+ri8BCPENZsVP8/WarV6T2oyb9SsqtZrDu+3YsmqnzvxqdawYO3MEZSqUZtzP0zhz3AmAchXLMGnBGHIaZ0erjWbT8u2cOno2zXmaNmvInAWTUKtV7NrxByuWbtSZb2RkyOr1C7G0qkhoaDg/9RuJ90tfihYrzOUbDjxz9wTg1i0XxoycprPszr1rKV6iCI3q2qY5Z5NmDZg1fyJqtYrdOw6yatmmBDlXrltAFasKhIWGM+jHUXi/jPkytfIVy/L70hkYG+ckOjqaVk278v59RJozAeS0rkbhqT+BWkXofkeC1ur+D+Tt0gyzCf2IDAgBIGS7PaH7T5O1QkkKzx6COmd2FK2WwNUHeHX8kl4yfZCZ/w+yN6hOgYmDQaXi1cGThG06oDM/V8cW5BvTn6jY/Ra+x47XB0/GzVflyE4J+w38feYKgbPX/GdzWTerz/S541Cr1ezbeYg1yzfrzDcyMmTp2rlUtqxAWFg4Q38cg4+3Hx27tGXQL33j6spXLEubxt144eXNQfvtcdPNzAty+I/jzJi4ME05azWuybAZQ1CrVdhdm207AAAgAElEQVTvPcGe1ft05lepXZlh04dQunwpZg6dzQX7iwBY1bNk2LTBcXXFShdj5tDZXDp1JU15Umry3CU4X76BSd48HNm17l/ZZmIKN65CrZm9ECoV7nvP82C1XaJ1xdvWpMmGEdi1nkLIfc90y6OuWIOs3w1GqFREXDxJxMn9CWoMajQii20vQCHa+zlvN81HXc6SrN1/jqtRFSrK2w1zibr37zyf+pQZbqWYHv5vG+dCiG3AcUVRDiZXm+y6VIJuM39kVc85hPuHMObYPB443sLfwzeuxvuxFxdtJxD5LoIGPVvQcUIPtg5bTslqZSlVoxxzW40BYNTBmZSpUwH3a4/TGgsAlUrFwNk/M73HFEI0ISy0W8INx+v4uHvH1Tx/9JzRbUcR8e49LXu2pvfEfiweGnMROLL+EFmyZaFlj9Z6yfOBUKnoNfMnFvWcSah/CFOPLeCe4038PHzial4+9mSm7Vgi3kXQpGdLuk3oxdphSwCIeBfBtDaj9ZrpA5VKxcR5oxnUbQQBmkD2nNzM+dMXee7mFVfj7+vPlBGz6TNE54vAePf2HZN/mclLTx/yF8zH3tNbuOJ0nb9e/52mPPMXT6Vrx374+QZw2ukgJx3O4eb6LK6mR++uhIe/plZVGzp2bsPUGaP5qd9IALw8X9KkYcdE193WtgX//PNPqrN9mnPeoil069gfjV8AJ50OcPqEk07OH3p1ITz8FXWrtaJDpzZMnj6aQT+OQq1Ws3rDQoYNGsfjh67kzZuHyMgoveRCpaLwzJ/x7DmFSP8QLI4t4bXjdd57eOuUhR+/iN+09TrTot++x3vUEiK8NBgUMKHM8aX85XyX6Nf62WeZ+f8AlYoCU4bi238ikQHBFD+wgn+crhHx7KVO2d8nnJNs4JoO782bmw/+07lUKhWzF06iR6eBaPz8sTu7D8eTTri7Po+r6d6zE6/CX9OoRltsO7ViwvSRDO0/hiMH7Tly0B6AcuXLsHn3Ch4/dAWgtXXXuOXtz+3nhF3aXuSrVCpGzP6F0T+MI0gTxDr71Vw+fYUX7h/3W6BvIPNHLaT7oG46y9674sKAljGNOeM8xuy+tJ2bF26nKc+X6NimBT90bs/EWYv+tW1+SqgEtef04fT383mjCaWdw0xenr7NK3fdb2o3yJGV8j+2JOiOR3oHItsPw/hn6XiUsGByTFpJlMtVojUfn09VAXOytP6OfxaMhDd/I4zzAKB1deGfmbEvtrIbYzx3K1GP/73nU0qeHNaiByWsLAh+EUCIdyDaSC137K5QxaamTo371UdEvovpBfS6606eQqaxcxQMsxhiYGiAgZEhagM1r4Ne6S1bGasyaLw0BLwMICoyikt2ztSyqa1T8/DqAyLevQfA7a4rpmamcfMeXL7P27/f6i3PB6WsLAh84U+QdwDayChu2F2i6if77OnVh0TE7rNnd93IW8g0sVXpXaWqFfD29MH3pR9RkVGcPHKGxi0b6tT4efvj/uQZ0dG6N3J68dybl54xDauggGBCg8PIa5onTXmqVa+C1/MXvPDyITIykiOH7GndtplOTes2Tdm/J6bH1e7IKRpa1012vTlyZGfw0H4s+X1tmvJ9ULV6FTyfv+Tli9icfzrQsk1TnZqWbZpyYG/Mu0rHj56igXUdABo3rc/jh65xDZOwsPAE+za1sluVIeKFhgjvAJTIKMLtnMn1yf9AUiI8/Yjw0gAQFRhKVMgrDExy6SUXZO7/g6xVyhH5UkOkjz9ERvHa4QI5miZ/XH2QpYIF6nx5eHP5zn86l1X1ynh5fjjuo7A7dAKb1k10amzaNOHgvpgvBXQ46kj9RgmPvw6dW3P0T4cE00uUKoZpfhNuXE1b4+kbq3L4evmheakhKjKKc0fPU9+mvk6Nv08Az594onzmf8+6bSOuO93kfew1499Qw6oyuXMZ/2vbS0y+qqX5yyuAv18GER2pxfPoNYq1rJ6grtrYLjxcexztu8h0zaMuWY7oID+UYH/QRhF58wIGVvV0agwbtiHC6Ri8iekcUv4KT7Aew+oNiXp4CyL+vedTn6JR0vUno2SaxrkQorcQ4r4QwkUIsVMIYSuEuC6EuCuEOCOEKBhbZx379af3YucZCyEaCyGOx1vXKiFE39jfpwohbgohHgohNgghhL6z5y5oQphfSNzjME0IuQvmTbK+brcmPD5/DwDPO+64X33EnJvrmXtjPU+cXQh45pvksl/KpJApwX7BcY9DNCGYFkz64t68ewvuOKX/K+i8BU0IjZcrVBNK3s/katStGQ/Of7yYGmYxYuqxBUw+PI+qNrX0mq2AWX78/QLiHgdqgiholv+L11OpankMDQ3x9krb82lmXhBfX/+4x36+AZj9j737Do+i6vs4/DmzuxCSAKmkAqFJCSUU6VKlSG9KEUQBsWGhd1SQagERUJBHUQSkN+mE3nuo0gkhvSeEhGR35/1jQ5JNkZINCbzn9uIyu/PbnW9mp5w9c2bi5mJW4+rmQmCgqRFpMBiIi4vHwcG0DpYq7cmeg+vZuGUp9RukH0zGjP+cBfN+IzExKVf50nK6lSAoQ87goKw53dxcCMqQMz4uHgcHO8qW90IFVqz9lZ371/LJZwMtkglA5+JISoZ1LSU4El0261rxNxpSYdtcSi0Yg87NKcv0IjUqIHRakv1Dskx7VgV5O9CWcEQfEp72WB8ake1ys23dmNIbfsZtzni0rqnLTQicRw8m4tvFWepftlyu2az3Llm2z/Qa03p/H3sH8y/tHbu2ZeO6bVnev3P3dmxevz3L80/L2c2J8OCwtMfhIeE4uz39F70WnZqxZ8OeXOd50Vi72pMQFJX2OCE4CmtX8+O8g3dprN0cuLf7XJ7nEXZOGKPStwM1OhzFzvzzVFw8UVw8sR49G+uxP6LxrpPlfXR1m5FyYm+e55WeToEY1iKE8AbGA41UVY0QQjgAKlBfVVVVCDEIGAUMB0YAn6iqelgIYQs8rmUxT1XVyanzWQp0ALIfKJaeZzAwGKCZQ228i5Z7XP6sT+bwhevVLo0pVb0cP/b8CgCn0i64lPdgQn3TKaZP/5pAubrnuXniyn/O80llly2nMVpNuzajXPXyTHhrrEXm/Z+eIleDLk3wql6OGT0npj03ouEHxIRF41zShVErvuLev/6E3w3N9vUWiPbU49qcSjgy9adJTPjsm1yPiXuSzzCnmtCQMGp6Nyc6OobqPt78uWw+jeu3x8urJGXKlmLiuOmULOWRq3z/mYEnyQlajYZ69WvRtvmbJCYmsXrj7/idu8ShA8csESzrc5mWX9zuE8Rs2o+arMfh7baU/P4LbvWZkDZd62xPqR+GETBiTpbXWjpbQdkOniTb/X3HiN+yDzUlheI92+E6fQT33huDXe8OJBw4gT4kIst7vGy5crN9PuJTuxqJiUlcu5J1KESnbm354sNxlkiaTYaneweHEg6UrVSGE/tPWSDPC+Zxx3khqPtVXw4NXZi1Lk/yZPdkpg9Uo6C4ePDguxEIe2dsRn3P/S8HQ6JpWJ4o7oDi4YX+0ov7ecr7nOetFsAaVVUjAFRVjQI8gR1CiAvASMA7tfYw8IMQ4jPATlXVxw1MbZ7aA38hdT7ej6lHVdVFqqrWUVW1zuMa5gAxIZHYu6d/Y7V3cyQ2LDpLXcVG1WgzpBsLB81Cn2yKXaNNXe6cvU7yg4ckP3jIpX3nKFOzwmPn+aQigyNwck/vBXR0cyQqLCpLXfXGNegx5C2mD/wmLVteig6JxCFDLgc3B2KyyVWlUXU6DOnOj4Omm+WKSV2+4QGh/HvsEqW9y1gsW2hQOK7u6T1fJdycCXuKg7mNrTXz/vqOeTMXceHMpVznCQoMwcPDNe2xu4cLISFhZjXBQSF4eLgBoNFoKFasKNHRMSQnpxAdbTqVef7cJe7cvku58mWoU7cmNXyqcvq8L/9sX0658l5s+OfP3OUMCsU9Q043dxdCgsMy1YTgniFn0dScQUGhHD18kqioGBITk/DddYDqNarkKs8jKSER6DKsazo3R1IyrWuGmHjU1PUrasVOilQtnzZNsS1Cmd+/JOT7v3hw9qpFMj1SkLcDfWgEWtf0M0ZaFyf0mbIZY+JRU0yn72NXb6ewt2nfZeVTGbs+nSiz+w+cRw2iaOeWOA1776XMFZzNeh+WZftMrzGt97bERKcPX+zULfshLZW9X0Gj0XDBL/fXIIUHh+PsViLtsbOrMxEhkf/xiqyad2zKwe2HMegNuc7zonkQHIWNu0PaYxs3Bx6Eph/ndbZW2FXypO2a8fQ4NhvnWuVo+fswHKtbbpvMSI2OQHFI3w6EvTPGmKgsNfpzR8BgQI0IwRhyD8UlvTNGV6cJ+rOm6VLBUlAa54Ksfc0/Yer1rgZ8AFgBqKo6AxgEFAGOCSEqAXrMfxcrACGEFbAA6JH6Pr8+mmZJ/n43cfZyxdHTGY1OQ62ODTm/y/ybqKe3F72mDWLhoFncj4xLez46KILy9aqgaBQUrYYK9SoTkuFisNy67ncdtzLulCjpglanpXHHJpzcdcKspox3WT6a/gnTBk4hNtJy493/y22/G5TwcsPJswQanZa6HRtzNtMyK+Vdhv7TPmDuoBnEZ1hm1sVs0BYynfSxtS9KhdqVCLpuuWV26dwVSpX1xKOUG1qdlrZdXmf/zie7Q4dWp2X27zPYvHobuzZb5lTh2TMXKFPOi1KlPdHpdHTp1p7tW81PK2/fuoeefboC0LFLm7QeZ0dHexTFtGmU9vKkbDkv/O8EsOR/K6hW6TVqV29Jh7Z9uHnjDl06vJOrnOfOXKBsudKUKu1hytm9HTu3mS+Dndv28lbvzgB06NyGw6k59/keorJ3RYoUsUKj0dCg0atmF5LmxgO/6xTyckfn6YLQabHr2IS4TNuA1jn99HSxVnVJumm6WFTotJReOJ7odXuI3XrYInkyKsjbQdKFq+hKu6P1cAGdlmLtmpKw1/xMhsY5vbFi26I+ybdMF6OFjJrF7ZbvcPv1/oTPWkz8Rl8ifvj9pczld+YiZcqWpmQpD3Q6LR27vcGu7fvManZt20ePXp0AaNe5FUcOpq9/Qgjad27N5nVZh6507t6OTdkMdXkWV/2u4lnGA9eSrmh1Wlp0bsaRXU93d46WnVvgu/H/35AWgIhztyhWxhXbks4oOg1lOtcnYGf6ELOU+ET+rvYRa+oPZU39oYSfuYnvez/k2d1aDHeuopTwQDi5gkaL7tWm6P2OmtWknD2CpqIPAMK2GIqLJ2p4cNp0bd3mL/yQFqOq5um//FIghrUAvsB6IcRsVVUjU4e1FAceDdbt/6hQCFFOVdULwAUhRAOgEnAaqCKEKIyp8d0SOER6QzwidQhMDyDXd2fJzGgwsmrSb3zy5ziERuHYqn2EXL9H+6FvcvfCLS7sPk2XsX0pbG3FwAWmO2hEB0aw8P1vObv1GK80rMq4Hd+hqipX9p/joq/lLqAyGoz8OvEXvlz6NYpGwXflbgKu3aX3sLe5ceE6J3edoP/497CytmLkz2MACA8KZ/rAbwCYumYGHuU8sbKx4tfjvzN/5FzOHThrkVzLJi1m+J8TUTQKB1ftIeh6AF2G9uLOhRuc232Kt8a+Q2FrKz5eMBxIv1Wce3lP+k/7AKOqogjBlp/Xm93dIrcMBgPTx/3Azytmo2g0bFjxDzev3ubjUYO4dO5f9u88hLdPZWb/Np1idkVp2qoxH48cSLemfWnTqSW16vtQ3L4YnXq2A2DS51O5eul6rvKMHTGZVesWo2g0rPhrLVf/vcHocZ9x7uxFdmzbw7Kla1iw6FtOnN1JdHQsgweY1rMGjV5l9LjP0OsNGI0GRgz90qzHzpIMBgPjRn7DirWL0WgUVvy1jqv/3mDUuE85d/YiO7ftZfnSNcxbOJOjZ7YTEx3LBwNMn21sbBwL5y9h+57VqKqK764D7N6530LBjARN+oWyf34NGoXoVbt5eP0uLkPfJvHCdeJ2n8DpvY4Ue70eqsGAISaeeyN+BKB4+8bY1vVGa18U+x6mi3ADRswh6bJlDrgFeTvAYCT8mwV4Lp4KikLcup0k3/DH8dN+JF28TsLeY9j37YxNi/qgN2CIjSdk7PeWm/8LkstgMDBx1DSWrvkFjUbDymXrufbvTYaN/YQLZy+xa/s+Vv61jjm/TOfAqS3ERMcyZNCotNfXa1ib4KAQ7vpn/ew6dGlD/54fWyinkR8n/sS3y2agKArbVm7nzjV/3hvRn6t+1ziy6ygVa1Tkm8VfYVvclgatGvDusP6813IQAK6eLji7O+N39LxF8jyNkV/O4OTZ88TExNGyS18+HtiP7h3bPNcMqsHIsQl/0Gr5KISicGPlfmKuBeIzojuRfrcJ2GXZC58fy2gkafk8rL+YhhAKyYd3YAzyp3CndzD4X0PvdwzDpVNovWtj8/Wvpvo1v6ImxAMgHF1Q7J0xXHv+n6f0eKKg3CNSCNEf0/AVA3AWWA/MxtRAPwa8qqpqMyHET0Dz1LrLwLuqqj4UQswCOgPXgWRgk6qqS4QQ3wC9gDtAAOCvqupXT3orxSFePQvGAsok0PggvyPkyE4plN8RcnQmKfjxRfkgKPHpTi8/TxpRUE6wZbXTrvzji/LJbArmdjC2iGVuA/n/zeuhlrtQ35LKWrs8viif7Dq3KL8j5GhZjUn5HSFb3dqFPb4oHxX7dafFb6qRG695tMzTNtrBQN98+X0LSs85qqr+AfyR6emN2dR9msPrR2G6aDTz8xOACdk8/+4zBZUkSZIkSZKkPFJgGueSJEmSJEmS9KTy817keangnq+WJEmSJEmSpP9nZM+5JEmSJEmS9MKRPeeSJEmSJEmSJOUp2XMuSZIkSZIkvXAKyh0HLU32nEuSJEmSJElSASF7ziVJkiRJkqQXzss65lw2ziVJkiRJkqQXjvqSNs7lsBZJkiRJkiRJKiBkz7kkSZIkSZL0wnlZLwiVjfPHmPpaRH5HyNZWX9f8jpCj5uUD8ztCjr67Wzq/I2Rri1Gf3xFyFPUwLr8j5GgZtvkdIUdaCuZnqqoivyPkSIiCe6DVKbr8jpAtJ411fkfI0bIak/I7Qo7e9puc3xGyZYwIyO8IUgEgG+eSJEmSJEnSC+dlvSBUjjmXJEmSJEmSpAJC9pxLkiRJkiRJL5yXdcy57DmXJEmSJEmSpAJC9pxLkiRJkiRJLxw55lySJEmSJEmSpDwle84lSZIkSZKkF478C6GSJEmSJEmSJOUp2XMuSZIkSZIkvXCM8m4tkiRJkiRJkiTlJdlzLkmSJEmSJL1w5JhzSZIkSZIkSZLylOw5txBttVex6vcJKAop+7by8J+/s9To6jalcLf+oKoY7t4k8edpAAjHEhQZOBzFwRmAhO/GokaEWiybW7Pq1JrSD6Eo3FyxjyvzNmdbV7J9XRr/+jk72k4g6vxtCtnb0njR5zj4lOX2qgOcHv+HxTIBFKpbl2KfDQFFQ+KWLSQsW242vUjbthT9+EMM4REAPFi3nsQtWwCw/XAwhes3ACDhzz9J2rPXotkqNq1B50nvoGgUjq/cy96fN5lNbzKwHfV6NcegN5IQFceqUQuJDjTlbD+mN5Wb1wRg10/r8PvnmEWzNW5en3FTh6NoFNb8tZHFP/1pNr1O/ZqM/WYor1Qpz/DBE9j5z560aYv+/pEataty5rgfH/UdlusszVs25puZ49FoFJb9uYafZv9qNr1QIR3zFs6kuo830VExDH5vGAF3AylZyoODJ7Zw8/ptAE6f8mPU0K+wsbVh07a/0l7v5uHK2pWbmDh2eq6zPvJK0xp0mvQOQqNwcuVe9mX6bOu9/ToN+rVCNRp5mJDEurGLCbsRaLH5Z+bd1Idek95D0SgcXOnL9p83mE1vNbADjXu1xKg3EB8Vx5JRC4hKXdcc3J14Z8aHOLg7oqow971pRN4Lt1g268a1cRn/ISgKsWu2E/XrarPpxbq+jvPIQehDTXlilm0mds2OtOmKjTVeWxdyf/cRwqb8bNFcJcZ9lJYrevEq81xdWuE0ciD60EhTruWbiVuz3TzXlkWmXN8syHWeJi0aMnHaCDSKhpV/rWfh3CVm0wsV0vHdgilUrV6Z6OgYPhs0hsCAYHQ6Ld98P4FqPpUxGlWmjP+W44dPAzB83Cd07dmeYsWLUd2rca4zAvg0rcV7Xw5C0Wjw/XsnG35eaza9w6DOtOzVCqPeSFxULPNHziUiMByvKmV4f+pHWNtaYzQYWTtvFUf+OWSRTNnxaFadupNNx6zrK/ZxYX72x6zS7V+l+aLP2fzGRCLP386zPDmZMO0HDhw+gYO9HRv++uW5z//QmYvM/HUlRqORbq0aM7DHG2bTg8IimfTTH0THxlO8qA3Thg7E1ckeAJ+uH1ChtAcArk4O/DRhyHPPbwkv65jzfG2cCyE6AVVUVZ2Rw3QfwF1V1a15NP+vgPuqqn6XuzdSsOr/GQkzR6FGhWM7eQEpZ45iDPJPK1FcPCjcsTf3J38GD+4jitmlTbP+YDQPNy1Hf/E0FLYCC65sQhHUnvYue3tNJzE4itZbpxC44wxx180bG1obK14Z2IaI0zfSnjMkpXD+29XYVSxJ8UqeFssEgKJQbOjnRA8bgSE8HMdFv5B06DAGf3+zssQ9e4mf86PZc4Xr10dX4RUiBw5C6HQ4zP2Rh8eOoz54YJFoQhF0nfwei/pOIzYkks83TeXyrtOEZmigBV6+w5yO40lJSqZB39dpP7YPfw2ZS+XmNfHwLsMP7cagLaTjo5WT+HefHw/vJ1okm6IoTJw5ioFvDiE0KIxVO/9g746D3LyWfmAKCgxh7GeTGfBx3yyv/23+X1gVKUzPd7pZJMuM7yfxVpcBBAWGsmPvanZs3cO1qzfTavq804OYmDjq12xDl+7tmPj1cAa/Z/pS4H/7Li1f62r2ngn3E8ye27l/LVs278p11keEIugy+T0Wp362Q1I/24yN73MbD3N82W4AKr9emw4T+/Fb/2x3URbIo9Bn8kBm951CdEgU4zdNx2/XKYJv3EuruXv5NlM7jiY5KZmmfVvTY2w/Fg2ZDcCAH4awZd46rhw6T2FrK1Sj0XLhFAWXSZ9wb8A4UkIjKL36R+7vOU7yzbtmZfHb9ufY8Hb6vB+JJy9YLlNqrhITPyFwYGquVXNJ2HssS6772w7k2PB2/OwdHlgol6IofDVzNP17fExIUCjrd/2F7/b93MiwTb75dhdiY+JoUbczHbq2ZvSXn/PZoDH07GfaDts16Ymjkz2/rZxHl9f7oqoqvjsO8Of/VuJ7fENOs37qnIOmfMDktycRFRLJjE3fc2r3Ce5dD0iruX3pFqM7DCM5KZnWfd+g39h3mT3kWx4mPuSnobMJuROMfQkHZm35gXMHzvIgLsEi2TISiqDe1P7s7D2DB8FRdNg6mbs7TxN7PcisTmtjReUBbQg/cyOHd8p7Xdq1ok/3ToybkrsmxLMwGIxMW7icRV8PxcXRnt4jptGsbg3KlXJPq/n+99V0bF6fzi0acvz8v8xduo5pQwcCULhQIVbPmfTcc0tPxmLDWoTJU72fqqqbcmqYp/IB2j1ljuf+hUNTrhLG0EDU8GAw6Ek5thdd7YZmNYWat+fh7k3w4D4AalwMAIp7aVA0poY5wMMkSH5osWwONctx/04oCXfDMaYYuLvxGJ5tamepqz6qB1cW/IPhYXLac4bEh0ScuIbhYYrF8jyiq1wJQ2AghuBg0OtJ8t2DVeNGT/RajVdpkv38wGBATUoi5eYNCtera7FspXzKE+kfQlRAGIYUA+c2H8W7dR2zmptHL5OSZFpW/mdvUNzVAQCXCh7cPH4Fo8FIcuJDgq74U6lpDYtlq17Lm7u373HPP4iUFD1b1++kRdsmZjVBAcFcu3wDYzYNtWMHT5Jw3zJfYmrVrs7tW3fxv3OPlJQUNqzbStv2Lc1q2rZryarlpsbF5g07aNy0wRO/f5mypXFycuDYkVMWyQtQMtNn67f5KFUyfbYZv0gVsi5s0S/LmZXxKU+4fwgRAWEYUvSc3HwYn0x5rh69RHLqunbr7DXsU9c1t/KeKBoNVw6dN+V+kJRWZwlW1V8h5W4QKfdCIEVP/Nb92Las/8SvL+xdHo2jPQmHz1gskylXRVLuBqflitu6H5sWT75eFa5SHo2THQ8slKtGrar4375HgH8gKSl6/lm/g9ffaGZW8/obzVj39z8AbNvkS4PXXgWgfMWyHDl4AoDIiGjiYuOp5lMFgHOnLxCeekbCEsr7VCDkTjBhAaHoU/Qc3nyQV1vVM6u5dPRC2jp0/exVHN2cAAi+HUTInWAAosOiiI2IpZhDMYtly8ipZjni74RyP/WYdXvjMUplc8yqNaoHF3/+B0OS5Y9PT6qOTzWKFyuaL/O+eP02pVxL4OnqjE6npe1rr7L3hJ9Zza2AYOpVrwxA3WoV2XvcL7u3eqGpefxffslV41wI4SWEuCKEWACcAfoJIY4KIc4IIVYLIWxT69oJIf4VQhwSQswVQvyT+vy7Qoh5qT+/KYS4KITwE0IcEEIUAiYDPYUQ54QQPYUQNkKI34QQJ4UQZ4UQnTO8z2ohxGZgZ+pzI1Przgshvs6QebwQ4qoQYjdQMTe/f9p72juhRqWfSjZGhSPsncxqFFdPNG6e2Ez8EZsvf0JbzbRzVtw8UR8kYP3ZV9hO+QWrXoPh6b7j/CdrVwceBEWmPX4QHEURN3uzGvuqpbF2dyRo91mLzfdxFCdnDGHpy8wQHo7i7JylzqppExx//x92k79GKWGarr9509QYL1wYUbw4hWrWRClRwmLZirvYE5NhmcUER1LcxT7H+npvNePffaadXtAVfyo1q4HOqhDW9kUp36AKdm6OFstWwtWZkMD0IU+hwWG4uGVdbs+Dq7sLQYHBaY+DAkNwdXMxq3FzK0Fgao3BYCA+Lh4HB9NZo1KlPdl9cC8rpF4AACAASURBVB3rtyylXoOsB9+uPdqzcf02i2bO/NnG5vDZNujXilH759BuTB82fmXZ4VwZ2bk4EJUhT3RwFHYuOa8vjd9qycV9pu3UpawbiXEJfPTLCCZumUWPsaZhAJaidXEiJTh9G9WHRKDNJlvRVo3x2rgA9x/Ho3VN3e8JQYnR7xP+7WKL5UnLVcIRfUiGXKER6LLJZdu6MaU3/IzbHPNczqMHE2HBXC5uzgQHhaQ9DgkKw8XNfH/k6uZMcKCpxrQd3MfewY5/L13j9bZN0Wg0eJZyp2qNyrh5mG9DluLg6khEcHpjPzI4AgfXnNe1Fj1bcXbf6SzPl69RAW0hLaH+Idm8KvesXe1JCIpKe5wQHIW1q/k26uBdGms3B+7tPpcnGV4EoZExuDg5pD12cbQjLDLarOaVMiXZfdT0JdT32FkSEpOIiTN1ECYnp9Br2FTeHjmdPcee37FfejKW6GWuCLwHTALWAa+rqpoghBgNDBNCzAIWAk1UVb0thFiRw/tMAtqoqhoohLBTVTVZCDEJqKOq6hAAIcQ0YI+qqgOEEHbAidRGNkADoLqqqlFCiNZABaAuIIBNQogmQALQC6iZ+rufAbLsfYQQg4HBAHPqVeTdCh7/vQRENs9l7m1TNCguHiRMG4ZwcMZ2whzixw4ERYO2YlXiJ3yIGhmK9ZCJ6Jq0IWW/hRolj8smBDW/6svxLxZaZn5P6gmWWdKRIyT6+kJKCkU6daL4uLFEfzGM5JOneFipEo4L5mOMiSHl0iUwGCyYLWu4nDpPa3VpjGf1sizoORmAawcvULJ6OYas+5qEyHj8z1zHYMFs4imy5bVsomQNk0Pe0JAwanm3IDo6huo+3ixZNo8m9TtwPz79NHmX7u0Y8sHoPA+d3fI7unQXR5fuwqdTQ1p+2pVVwy03XvoxcXL8QOt1eQ2v6mX5tueXACgaDeVfrcyU9iOJCopg8LyhNOrRjEOr9mT7eovIFO3+3uPE/7MfNSWF4j3b4TpjOPfeHYtdnw4k7D+JPsRyPb9psv0MzYPd33eM+C370nNNH8G998Zg17sDCQdOWDRXdtvkk20HKquXbaTcK2XYsPsvAu8Fc+aEn0X3F2YRstnpZl5uj7zWtRnlqpVnUs+xZs/blbDn09lDmTf8xxxfm2vZLk/z6XW/6suhoc/5mFXgZF3+mdfF4e/2YPqiFWzyPUIt7wqUcLRDozF9gd+xeAYlHO24FxLOoIk/UKG0ByXdLNfJ9bzIMec581dV9ZgQogNQBTicuoIUAo4ClYBbqqo+GoC3gtSGbyaHgSVCiFWYGvnZaQ10EkKMSH1sBZRK/XmXqqpRGepaA4++DtpiaqwXBdarqvoAQAhhfiVYKlVVFwGLAGL7tXzsJ69GRSAc0nsvFQdn1JhIsxpjVDiGm1dMQzHCQzAGB6Bx8USNCsfgf8M0JAZIOX0YTfkqFmucPwiOwto9vXfE2s2BxJCYtMc6WyvsKpWkxdoJABRxLs5rS4Zz8N3vicrDC2yM4eFoSqQvM42zM8YI8wOmGheX9nPiP/9Q9MP01SZh6V8kLDVdOFh84gT09+5hKbEhUdhlWGZ2bo7EhUVnqavQqCoth3Th556TMSTr0573nb8B3/mmoRx9fhxCxG3L9TCFBofhmqFnzcWtBGEhlrsA8GkEB4bi7uGW9tjdw5WQkDDzmqBQPDzcCA4KRaPRULRYUaKjTetfcrLp/+fPXeLO7QDKlS+D39mLAFSpWhGtVsv5c5csmjnzZ1s8h8/2Eb/NR+n6zUCLZsgoOiQKhwx57N0ciAmLylJXuVE12g/pxrc9v0Sfuq7FhEQScPk2EQGmZX5u50nK1qwAq7K8/JnoQyPQZTgro3V1Qh+Wab8WE5/2c+zq7TiPGABAEZ/KFKntjV2fDghrK4ROhzEhiYgffrdILq1rhlwuTugzLbPMuZyGmz5DK5/KFKldFbveHVGsrUCnxfggMVe5QoLCcHN3TXvs6l6C0EzbZEhQGG4eroQEh6VuB7bERMcCMHXC92l1q7f+zp1MY+ctJTIkAie39DO6jm5ORIdmXdeqNapB9yFvMumtcWnrGkAR2yKM+30Sf3+3jOtnr+ZJRjAds2zc03uEbdwceBCavo2ajlmetF0z3pTLuTgtfx+G73s/5MtFofnFxdGe0Ij0zy80MgZnBzuzmhKOdswe+xEADxKT2H30DEVtrNOmAXi6OlOn6itcuRXwQjbOX1aWOAf6qKtLYGog+6T+q6Kq6kCy7yPNQlXVD4EJQEngnBAiu/NtAuieYR6lVFW9kinHo7rpGerKq6r6v0ezetpf8HEMt/5F4+qBcHYFjRZd/eaknDliVqM/fRhtZR9TONtiKK6eGMODMdy6irApiihaHABtlZoYA/2zzONZRZ27RdEyrtiUdEbRaSjVuT73dqafLEiJT2Rd1Q/ZXO8LNtf7gogzN/K8YQ6Q8u9VNJ6eaNxcQavFqmULHh42X2aKY/oOunCjhuj9Uw9aioIoZhrvqC1bFm25ciSftNy45AC/mzh5ueLg6YxGp8GnYwMu7TI/weLu7UX3aYP4fdB33I9M/xIhFIG1nS0AbpVK4V6pFNcOnrdYtgtnL1O6bEk8Srmj02lp17U1e3cctNj7P42zZy5QtlxpSpX2QKfT0aVbO3ZsNe+13bF1D2/16QJAxy5tOHTAdOcaR0d7lNQhGKW9PClbrjT+d9IvTOvWoz3r12yxeOZ7fjdx9HLFPvWzrdGxAVcyfbaOXukNrUotahJxJ29O3wPc8btBCS83nDxLoNFpebVjI/x2ma/LJb296DttMPMGzSQ+w7p22+8m1sVtsE0d+1upYVWCrlvuS2rShWvoSruj83ABnZai7Zpyf4/5nYc0zunDDWxb1Cf5pukzDB45i1st+nOr5buEz1pM3MbdFmmYm3JdRVfaHW1qrmLtmpKwN3Ou9H2HbYv6JN8y7TtCRs3idst3uP16f8JnLSZ+o2+uc50/ewmvsiXxTN0mO3Rtg+/2/WY1vtv3061XBwDe6NSSowdPAmBVxIoi1lYANGpaD73BYHYhqSXd8LuOWxl3SpR0QavT0qjja5zcddyspox3WT6Y/jEzBn5DXGRs2vNanZZRi8axf+1ejm49nCf5Hok4d4tiZVyxTT1mlelcn4Cd6dcHpMQn8ne1j1hTfyhr6g8l/MzN/3cNcwDvCl74B4dxLzSClBQ92w+epFld8+ubouPi0649WrxmG11bmq7rirufQHJKSlrNuSs3KVfSjRfRyzrm3JIXTx4D5gshyquqekMIYQ14Av8CZYUQXqqq3gF6ZvdiIUQ5VVWPA8eFEB0xNdLjMfV2P7ID+FQI8amqqqoQoqaqqtkNltoBTBFCLFNV9b4QwgNIAQ5g6p2fgel374hpyE3uGI0k/vkTNiNnmm6leGAbxkB/Cnd7F8Ptq+jPHkV/4STaanWwnfEbGA0k/b0I9b7pQJu0YiE2Y74DAYY710nea7lGiWowcmr8EpotH43QKNz6ez9x1wKpNrI7UX63Cdz53xdFdTw+B51tEZRCWjzb1GFv7xlZ7vTyTAwG4ub8iP1334KikLh1G/o7d7Ad8B4pV6/y8PARrLt3p3CjhmAwYIyLJ3Z66rXDWi2O8+YCYEx4QOw3Uy06rMVoMLJ+0hLe/3Os6XZ7q/YRev0ebYb2IODCbS7vPk2HsX0obG1FvwWfAxATGMnv73+HRqflk9WmYQdJ9xNZPnQ+RoPl7qBhMBj4Zsy3LF45F0WjsG75Zm5cvcWnowdz8dwV9u44SFWfyvy0ZBbFihejeevX+HTUYDo26QXA0k2LKFu+NNY2Rdh7bjMThk7l8N5nu9WjwWBg7Igp/L3uf2g0Civ+WsvVf28watyn+J29yI5te1m+dA3zFs3i2NkdxETH8sEA051a6jd6lVHjPsWgN2AwGhg19Ku0nkSATl3foE+P7E6w5Y7RYGTjpCUM/HMsSobPttXQHty7cJsru0/TsH9rKjSqhkGvJzE2Ic+GtDzKs3zS//jiz/EIjcLhVXsJun6PTkN74n/hJn67T9FjbD+srK34cMFwACIDI5j//kxUo5HVU5cyfNkkEIK7F29x8G9fy4UzGAmb8jOe//sGFA2xa3eSfOMujp/2I+niNRL2Hse+X2dsm9dHNRgwxsYTMvb7x7+vBXKFf7MAz8VTQVGIW7eT5Bv+qbmuk7D3GPZ9O2PToj7oDRjyOJfBYODrMTNZsno+iqKwZvkmrl+9xRdjPuTCucv4bj/AqmUb+H7BFPac2EhMTCyfv28aLuLoZM+S1fMxGlVCg8MY/tHEtPcd/eXndOzeliLWVhw6v41Vf21g7qxnP1wZDUYWT1rIhD+/QtEo7Fm1m3vXA+g5rA83z9/g1O4T9Bv3LlbWRRi+wDScLCIonJmDptKgQ2Mq1/XG1q4ozXq0AGD+iB+5c9nyDWLVYOTYhD9otXwUQlG4sXI/MdcC8RnRnUi/2wTssuwFxrkx8ssZnDx7npiYOFp26cvHA/vRvWOb5zJvrUbDuMG9+eirORiMRrq0bET5Uu7MX7aRKuVL07yeDycvXGPu0vUIAbWqvML4D3sDcCsghMk/L0URCkbVyIDubc3u8vIieVmHtYjcjBsTQngB/6iqWjX1cQtgJlA4tWSCqqqbUhvb3wIRwAnARVXVt4UQ75I6plwIsQ7T0BMB+AJfAPaYGto6YDqwCZgDNEytu6OqaoeM75Mh2+fAoNSH94G+qqreFEKMB94B/IF7wOX/upXikwxryQ9bfV0fX5RPmpfPu3tC59Z3dwtm78CWBzcfX5RPoh7GPb4on7xrXzO/I+QoCv3ji/LBCKv7+R0hR0IUyN0tAG+Ehz2+KB/UtLHwbW4tqIM+b+7oYglv+03O7wjZMkYEPL4oHxWu1PSJRkM8L+WcauXpTuNmxJl8+X1z1XOe2hNeNcPjPcCr2ZTuVVW1kjANRp8PnEqtXwIsSf05uxsvR2Xzfh9kkyPtfTI89yPwYza1U4Gp2f5CkiRJkiRJ0gshP4ee5CXL3Xfrv70vhDgHXAKKY4mhJJIkSZIkSZL0knkuf7BHVdXZwOznMS9JkiRJkiTp5aeqFvyryAXI8+o5lyRJkiRJkiTpMZ77n7qXJEmSJEmSpNwyyjHnkiRJkiRJkiTlJdlzLkmSJEmSJL1wcnM78IJM9pxLkiRJkiRJUgEhe84lSZIkSZKkF44ccy5JkiRJkiRJUp6SPeeSJEmSJEnSC+dlHXMuG+ePoY/R53eEbBkQ+R0hRw/vF9zVKpmC+QcL4lIS8jtCjrSKJr8j5OiPmHP5HSFH79j55HeEbDlVeJDfEXIkCvC5XE1EwQxnVYAP493aheV3hBwZIwLyO0K2FKeS+R1BKgAK7lYtSZIkSZIkSTkwvqQ95wWzK0CSJEmSJEmS/h+SPeeSJEmSJEnSC0eVd2uRJEmSJEmSJCkvyZ5zSZIkSZIk6YXzst6tRfacS5IkSZIkSVIBIXvOJUmSJEmSpBfOy/oXQmXjXJIkSZIkSXrhyGEtkiRJkiRJkiTlKdlzLkmSJEmSJL1w5B8hkiRJkiRJkiQpT8mecwvR1aqLzfufgqKQtGsLSWuWZ6kp1Lg5RXq/C6gYbt/k/ndTUJxdKDpuCigKaLUkbV7Hw+2bLJrNrVl1Xp3SD6Eo3Fixj0vzNmdbV6r9qzT59XO2tp1I1PnbFLK3pcmiz3D0KcutVQc4Of5Pi+ayavgqDiM+Bo3C/fXbiFvyt9l0m46tsf9iMIawCADiV27k/oZtFK5TA4fhH6XV6bxKET72GxL3HbFYtspNa9Bt0rsoGoWjK/ew++eNZtObD2xPg14tMOgN3I+KY/moX4gONOXsNOZtvFvURCgKVw+eZ+3XS3Kdp1nLxkyePgZFo2HF0rXMn7PYbHqhQjp+/Hk61Xy8iY6K4aMBw7kXEGT6XbxfYeYPX2Jb1BajaqR9i548fJhM5+7t+HTY+6iqSmhwOJ9+MJroqJh8zaXVaVm/dWna693cXVi36h++HDfjqZdZ85aNmTJjHBqNwrI/1zAvm2w//TKT6j5ViI6K4YMBwwi4m57t29lfU7SoLUajkbYt3uThw2TGTPicN3t1xs6uGOU86zx1psd5pWkNOk96B6FROLFyL/t+Nt8X1H/7dRr0a4VqNPIwIYm1YxcTdiPQ4jkeKaj7NV3Nulin5nq4awtJa7PJ1ciUS1VNuRJ+MOWyHZOe6+EWy+9vM3qtRQPGTx2BRqOw+q8NLJr7h9n0Og1qMv6b4VSsUp6hg8ezY7NvnmUBqN60Jv2+HICiUdj39242/7zebPobgzrSrNfrGPQG4qPiWDRyPpGB4Th6OPPFwlEoioJGp2Hnkq3sWbbTYrk03nWw6vURQlFIPrid5O0rs9Ro6zShcMd+gIox4BaJi2egqVgDq54fptUoriVJXDQN/TnLHQsOnbnIzF9XYjQa6daqMQN7vGE2PSgskkk//UF0bDzFi9owbehAXJ3sAfDp+gEVSnsA4OrkwE8Thlgs1+NMmPYDBw6fwMHejg1//fLc5vu8vaxjzv9fNs6FEF5AQ1VVs+7Rn4WiYPPhF8RNHI4xMpziPywk5fhhDAH+6SVuHhTp8TZxoz5BTbiPKG4HgDE6ktiRn4A+BayKYDfvd5JPHEaNirRINKEI6k7rj2+vGTwIjuKNrZO5t+M0sdeDzOq0NlZUHNiG8NM30p4zJKXg9+0a7Cp6YlfJ0yJ50igKDqM/Jezj0ehDw3H7az6J+4+QcvuuWVnCzn1Ez5xn9tzDU34E9zbtkJViRXHf+AdJx05bLJpQBG9OHsD8vlOJCYlkxKbpXNx1ipAMjaB7l+/wbcexpCQl07hvKzqPfZslQ36kTK1XKFunIjPajgTgizWTKV+/CjeOXX7mPIqiMPXb8fTu+j7BQaFs3bOSndv2cv3qzbSa3v26ExsbR+Pab9Cp2xuM/2oYHw0cgUajYe7CGXz+4VguX7yKvX1xUlL0aDQaJk8fQ7P6nYiOimH818N57/0+/DBzQb7mevgwmdZNuqe9ftveVWz9Z9czLbPp303krS4DCQ4KZfveVezctpdrGbL16deDmJhYGtRqS+du7Zjw1Qg+GDAMjUbD/EWzGPLB6NRsdqSk6AHYuX0fv/26nKOntz11pscRiqDr5Pf4te80YkMi+XTTVC7vOm3W+D678TDHlu0GoMrrtek4sR//6//0X1yeSEHdrykK1h98QfyXplzFvltI8onDGDPlsurxNnGjs+aKG52eq/hcy+5vzWMqfDljNO+9+QkhQaGs3fknvtsPcPPa7bSa4HshjPn0KwZ+3M/i889MKAr9p7zPjLe/JiokksmbZnF690mCrt9Lq7lz6TYTO4wkOSmZln3b0HvsO8wb8j0xYdF83W0s+mQ9ha2tmLFzDmd2nSQmLNoSwSjSZwgJs8egRkdgM/4n9H5HMQanHwuUEu4UfqMXCTOHwoP7iKKmz9Nw1Y+EyakdNdZFKTrtd/SXLXcsMBiMTFu4nEVfD8XF0Z7eI6bRrG4NypVyT6v5/vfVdGxen84tGnL8/L/MXbqOaUMHAlC4UCFWz5lksTxPo0u7VvTp3olxU77Ll/lLufP/dViLF9DHUm+mrVAZQ3AgxtBg0Ot5eGAPunqNzWqs2nQkaet61IT7AKixqT2Uer3pQAEInc7Uo2NBjjXLEX8nlPt3wzGmGLiz8RiebWpnqasxqgeXF/yD8WFK2nOGxIeEn7iGIcNzllKoakX094LQB5qWWcKOfRRp1uip38f69SYkHT6JmvTQYtlK+5Qn3D+UyIAwDCkGzmw+QrXWr5rVXD96iZSkZADunL2OnasjYPpTwrrCOrQ6LdpCOjRaDfHhsbnKU7N2Ne7cCuCu/z1SUlLYuG4rbdo1N6tp/UYLVq8w9e5v2biTxk3rA9C0RUOuXLrG5YtXAYiOjsVoNCKEQAiBtU0RAIoWtSE0JDzfc2VUpmwpnJwdOH7k6Q+2NWtX5/atu2nZNqzdSpt2Lcxq2rRrwarUbP9s3JGWrVmLRly+eDVDtpi0bGdO+REW+nTL6UmV9ClPhH8IUanrnd/mo3i3Nu+df3g/Me3nQtaF87TXqKDu17QVKmMMSc+VfHAPheqa5yrcuiMP82F/m1H1Wt743wkgwD+QlBQ9Wzbs5PU3mprVBAYEc/XyDYyqMYd3sZxyPuUJvRNMeEAohhQ9xzYfonarumY1V45eJDl1v3bj7DUc3Ez7NUOKHn2y6QuqrpAWoQiL5dKUqYgxPAg1IgQMelJO7kfr09CsRvdaO5L3boIHqZ9nfNYzfLrar6G/eAqSLXcsuHj9NqVcS+Dp6oxOp6Xta6+y94SfWc2tgGDqVa8MQN1qFdl73C+7t3ru6vhUo3ixovkdI88ZUfP0X355qRrnQoh3hBDnhRB+QoilQoglQoi5QogjQohbQogeqaUzgNeEEOeEEENzO1/F0QljRFjaY2NkOBpHJ7MajYcnGveSFJs5j2LfLkBXK32nqDg5U3zub9j/vprENcst2otj7WrPg6CotMcPgqOwdrM3q7GvWhobdwcCd5+z2HwfR+vshD4kfZkZwsLRlHDMUmfd4jXcVi7CadYkNC7OWabbtGlGwo49Fs1m5+JATFD6ZxATHElxF/sc6+u/1ZzL+0zL7s6Z61w7eokpJxfyzYmFXDngR+jN3A07cHVzISgwOO1xcFAorm4u5jXuJQgKDAHAYDAQFxePvYMdZct5gaqybM0itu9bzUefDQBAr9czdvgUfA9t4MyVfVSoWI4VS9fme66MOndvz6Z1258q0yNubunzfZTNLVM2twz5DQYD8XHxODjYUba8FyqwYu2v7Ny/lk8+G/hMGZ5WcRd7YjOsd7HBkRTLZr1r0K8Vo/fPod2YPmz66o8s0y2loO7XhKMThky5lMy53D1R3EtSdMY8is1agK6mea5iP/6G3f9Wk7TOsvvbjFzcShASGJr2OCQoDBe3Enkyrydh7+pIVHD67xoVHIm9q0OO9U17tsRv35m0xw5ujkzb/gM/HvuVf35Zb5lec0DYOWGMSv/Cq0aHo9iZHwsUF08UF0+sR8/GeuyPaLyzDinT1W1Gyom9Fsn0SGhkDC5O6cvIxdGOsEjz3/uVMiXZfdS0nHyPnSUhMYmYONOXiOTkFHoNm8rbI6ez59hZi2aTXm4vTeNcCOENjAdaqKpaA/g8dZIb0BjogKlRDjAGOKiqqo+qqrOzea/BQohTQohTf/gHZ56c3cyzPJWlQ0ujQePuSdy4z7n/3WRsPh2JsLEFwBgRTuxnA4ge3Aerlm0Rdjk3BJ/a47IJQZ2v+nL6a8uM8Hli2eTK/CU18cAxAjv0JbjnYJKOn8Fp8iiz6RonB3Tly5B49FSeZ8upg7JOl8aUql6OPYtM41adSrvgWt6DSfU/YmL9D3mlYVXK1a1s6ThZekwF2Rah0Wp4tX4thgweRZc3+vFG+5Y0blIPrVbLOwN60qZpD2pVbsaVS9f4dOj7+Z4ro87d3mDD2q1PlSk9WzafYaYVLNsaFbQaDfXq1+KT90fSue3bvNHhdRo3qf9MOZ7KE2wTAEeX7mJm0y/YOmM5LT7t+lzzFIz92hMsp9Rc8eNTcw0xzxX3+QBiPuxD4eZtEcUtuL/NmPIJto/nKdu+7hziNOrahLLVyrNl4Ya056KCIxnXdhjDm3zMa92bU8yp+PMLplFQXDx48N0IEn+dTpH+Q6GITfpbFHdA8fBCf8nCx4JsFlDm/cbwd3tw+uI13vpiCqcuXqOEox0ajalptWPxDP7+YTwzhw9i1v9WERAcluX9pNxRVTVP/+WXl6ZxDrQA1qiqGgGgquqj7uINqqoaVVW9DLjk+OoMVFVdpKpqHVVV6/Qv7fbYemNEOIpTeo+I4uiMMSoiS03y8UNgMGAMDcEYGIDibj6OW42KRH/3Droq1Z8k5hN5EByFtXv6N39rNwcSQ9K/+etsrSheyZNWa8fT5fhsnGqVo9mSYThUL2OxDNnRh4WjdU1fZpoSzhjCzXuwjLFxkGI6BX1//VYKVXrFbLp1q6Y82HsY9AaLZosJicTOPb3nxs7NkbhseoleaVSN1kO6sWjQrLRTvtXb1OXO2eskP3hI8oOHXNl3Dq+aFXKVJzgoFHeP9PXQzd2F0JCwbGpcAdBoNBQrVpTo6FiCg0I5dvgU0VExJCUmsWfXQarWqIJ3tUoA+N8JAGDzhu3UrueT77keqVK1Ilqthgt+zzZWPyjDfB9lC8l0YAwKCknLr9FoKFqsKNHRMQQFhXL08EmiomJITEzCd9cBqmfIlldiQ6IonmG9K57DeveI3+ajeLey/EWpjxTU/ZoaGY7mcbkiM+QKC8EQGIDiljWXIeAOWm/L7W8zCgkKw9Uj/ZDj6l6CsKccOmZJUSGRacNUwNQTHh0alaXOu1F1Og3pwQ+Dpqft1zKKCYsm8FoAFetaZptQoyNQHNLPigp7Z4wxUVlq9OeOgMGAGhGCMeQeiotH2nRdnSboz5qmW5KLoz2hEelZQiNjcHawM6sp4WjH7LEfsWrORD7r2wWAojbWadMAPF2dqVP1Fa7cCrBoPunl9TI1zgXZ9wM8zFRjcfrr/5pOo7q4glZL4SYtSDlx2Kwm+dghtNVqmkIUK47iXhJjSBCKozMUKmR63sYWXeWqGAIttwFHnrtF0TKu2JR0RtFp8Opcn3s7009VpsQnsqbqR2yoN5QN9YYSceYm+979gajzt//jXXMv+dJVtCU90LqblplNm2Yk7je/wl6T4XRikaYNSLljfrGoTdsWJGy37JAWgLt+N3H2csXB0xmNTkOtjg25sMu8R8bT24te0wbx66BZ3I+MS3s+OiiC8vWqoGgUFK2GcvUqE3rjXuZZPJVzZy5SplwpSpbyQKfT0blbO3ZuMz99u3P7Xt7s3RmA9p1bc/jAcQD2+x6msvcrjjNaxAAAIABJREFUWBWxQqPRUL9RHa5fvUlIcCgVKpbDwdHUa9ikWUNuXL2V77ke6dy93TP3mpuyXaBsudKUKm3K1qV7Ntm27eWt1GwdOrfh8IFjAOzzPURl74oUSc3WoNGrZheS5pV7fjdx8nLFPnW9q9GxAZd3mY+3d/JK/8JRqUVNIu+EZH4biymo+zX99X9R3DxRSphyFXota66UY4fQPcpVtDiKR0mMoUGITLm0lapitOD+NqMLZy/jVaYknqXc0em0tO/SGt/tB/JkXk/ilt8NXMu44VyyBBqdlvodG3Nm10mzmtLeZRgw/UN+GDiduMj0a2UcXB3RFTYtN+tiNlSoU4ngXA7Xe8Rw5ypKCQ+EkytotOhebYre76hZTcrZI2gqmjoPhG0xFBdP1PD0s9raus0tPqQFwLuCF/7BYdwLjSAlRc/2gydpVreGWU10XHzaNSmL12yja0vTtVNx9xNITu1cio6L59yVm5Qr+fjOPunpGFU1T//ll5fpbi2+wHohxGxVVSOFEDkPpoN4wHJXShgNJPwyh2Jff2e6tdfurRju3qHI2wPQX/+XlBNHSDlzAl3NVyk+/w8wGnnw+8+o8XFofepQdMDHmL5XCBLXr8Tg/3SNpP+iGoycHP8HLZePQmgUbv69n9hrgVQf2Z0ov9tmDfXsdDk+G51tEZRCWjzb1GFP7xlZ7vTyTAxGomb+RIn5M0BRuL9pOym3/Cn+YX+SL18j8cBRivbqSpGmDUy9X7HxRHw5K+3lGjcXNC7OPDx9PvdZMjEajKyZ9Bsf/zkORaNwbNU+Qq7fo93QN7l74RYXd5+m89i+FLK24r0FpksWogMj+PX9bzm39RivNKzKmB3fgapyZf85Lvr+9zJ+HIPBwIRRU1m+dhGKRmHlsvVc+/cmI8YOwe/cJXZt28vfS9cy95cZHDq9jZjoWD4eOAKA2Ng4Fi34g62+K1FR2bPrIL47TQ2E2bMWsG7LH6To9QQGBDP043EFIhdAxy5t6PfWRznN+omyjRv5DSvWLkajUVjx1zqu/nuDUeM+5dzZi+zctpflS9cwb+FMjp7ZTkx0LB8MGJ6WbeH8JWzfsxpVVfHddYDdO/cDMPHrEXTt0Z4i1kU4c8n0Ht/NmP/MOTMyGoxsnLSEQX+ORdEonFy1j9Dr92g9tAf3Ltzm8u7TNOzfmvKNqmHU60mMTWDl8J8tMu8cAhXM/ZrRwINFcyj6VWou360YAu5QpM8A9DdSc51NzTXvD1SDkcQlqblq1MF6wMem8TlCkLTBsvvbjAwGA5PHfsv/Vv2ERtGwZsUmbly9xWejP+DiuSvs2XGAaj5VmP/HtxQrXozmrV/js1GDaf9azzzJYzQY+WPSYkb9OQlFo7B/lS+B1wPoPqwXt8/f5Mzuk/Qe9w5W1lZ8tsC0nUYGRfDDoOm4l/ekz4T+jxYbWxdt5N7Vu4+Z45MGM5K0fB7WX0xDCIXkwzswBvlTuNM7GPyvofc7huHSKbTetbH5+ldT/ZpfURPiARCOLij2zhiuWf5YoNVoGDe4Nx99NQeD0UiXlo0oX8qd+cs2UqV8aZrX8+HkhWvMXboeIaBWlVcY/2FvAG4FhDD556UoQsGoGhnQva3ZXV7y2sgvZ3Dy7HliYuJo2aUvHw/sR/eObZ7b/KXcES/TPSKFEP2BkYABeHT1xT+qqq5JnX5fVVVbIYQO2A44AUuyG3f+SGTHpgVyAW07XTK/I+ToNde8683Lre+jsl50WhCsjb2Y3xFeSIbncJeLZ/WO3dMNE3peRlezwJfrPCIK8Lnc+kcS8jtCtupal8rvCDla0Pp+fkfIUeHh4/M7QrYUp4J7bAfQOZXNkxEIz8rG2itP22gJD+7ky+/7MvWco6rqH0COty9QVdU29f8pQMvnlUuSJEmSJEmSnsRL1TiXJEmSJEmS/n/Iz3HheakAn0SUJEmSJEmSpP9fZM+5JEmSJEmS9MJ5ma6bzEj2nEuSJEmSJElSASF7ziVJkiRJkqQXTua//PyykD3nkiRJkiRJklRAyJ5zSZIkSZIk6YXzso45l41zSZIkSZIk6YXzsjbO5bAWSZIkSZIkSSogZM+5JEmSJEmS9MJ5OfvNZc+5JEmSJEmSJBUY4mUdr1NQCSEGq6q6KL9zZFZQc4HM9iwKai6Q2Z5FQc0FMtuzKKi5QGZ7FgU1FxTsbFLOZM/58zc4vwPkoKDmApntWRTUXCCzPYuCmgtktmdRUHOBzPYsCmouKNjZpBzIxrkkSZIkSZIkFRCycS5JkiRJkiRJBYRsnD9/BXXsV0HNBTLbsyiouUBmexYFNRfIbM+ioOYCme1ZFNRcULCzSTmQF4RKkiRJkiRJUgEhe84lSZIkSZIkqYCQjXNJkiRJkiRJKiBk41ySJEmSJEmSCgjZOH8OhBCfP8lzEgghFCHExfzO8SISQjjkd4bsFNRcAEKI74QQ/9feuYdrPpZ7/POdTM7jUCJlxGwz9hCSY0ZRqCkUHaRCouvqSFsRW6KJooMO9i5KiTHsEiERtowxw5gGY8axdkQqpRyaGMe++4/n+c161zvvWjPJep9nvev+XNe63vf3/NZ7Xd/rPfx+93M/9/O9NymtoxOSTlqWsRJIWkfSnpL2kLROaT2tSNpS0iGSPi5py9J6gqDbRMwx/IkNoV1A0k22t2wbu9n2q0ppatHxMmB9YLlmzPaMcopA0jTgKNv3ldQxEJJeA7yC/u/ZWcUEZST9GpgHnAFc5kp+3LXqApB0MHAg6bM8AzjX9qNlVSUGuG7Mt71ZKU1Zw8HAZ4FfAAJeB0yx/f2SugAkfRZ4J3BBHnobcJ7t48upSkhaHdifJa8dhxTUtBAY8Pdoe0wX5SxG0t6Dnbd9wWDnhxJJhw123vbJ3dIyEDXHHMGysdzS/yV4rkjaF3gPsIGki1tOrQr8tYyqPnIWbh/gduDZPGygaHAOvBS4TdIc4LFm0Pae5SQlJE0FxpGCzdb3rHhwDowHdgE+AJwi6YfAD2z/qqysanVh+3TgdEkTSEH6fEmzgO/avrqEJkkfBj4CbChpfsupVYFZJTS1cTjwKtt/BZD0IuA6oHhwDuxL0vYEgKQTgZuA4sE5cCkwG1gA/KOwFgBsrwogaQrwADCVNOF6L+n7Voo9Bjln+iZfJWjelwnA1kBzb9+DwvfO2mOOYNmJzPkQIml9YAPgi8CRLacWAvNtP1NEWEbSXcBmtp8sqaMdSR8H7gceah23fU0ZRX1IugOYWFP2txOSdgbOBlYGbgGOtH19WVV16pL0AmB3UnC+HvAjYBLwmO13F9CzGrAGHa4bth/q/KruIekqYLLtp/LxC4FLbe9SVhlIugzY1/Yj+Xh14Gzbu5dV1jmbWQuSbrC97dLGgj4kXQG83fbCfLwqaZXmTQU1VR1zBMtOZM6HENv3AvcC25fWMgB3A6OBqoJzYG3gUFLG6/vA5RUFw7cC6wB/LC2knZzBfB+wH/An4OOkrM4WwHmki3bo6q/tZGBP4CrgC7bn5FMn5clrCWz7t5I+2n5C0poVBOi/B26QdBEpi/lWYE6z3F94Wf9J0qrblVnbrsBMSd/M2oqVkABTJX0QuISWa24FnyfAs5LeC/wP6X3bl76VwaJIeguwCbBCM2Z7SjlFixkLPNVy/BSpZKkYwyDmCJaRCM67QK6fOwl4CWnJUKQbcJF6vhYeB+blTFjrzaLkDQzbn5F0DLAbKZv5X5J+BHzP9m9KaJL0U9JNa1Xg9lxy0/qeFS+5Aa4nLUu/zfb9LeNzJZ1aSBPUqwvSZOszth/vcG6bbovJnEPK5N9I+s6p5ZyBDUuIauE3+a/hovxYsgyi4Sf5r2F6IR2deAr4MnA0fXXeNXyekEohvpH/TCqfek9RRUC+PqwE7AycDrwDmDPoi7rHVNKk9Cek92wv6ihvrDnmCJaRKGvpApL+D9jD9h2ltbQi6YBO47bP7LaWTkjanBScvwm4GtgOuNL2EQW0vG6w85WU3KiiFYbF1KqrQdIawEb0z8yV3ncR9BiSfgNsa/svpbUMF5oN0C2PqwAX2N6ttDZIzkDAjvlwhu2bS+ppqDXmCJadyJx3hz/V+COxfWauFx2fh+6y/XRJTQCSDgEOAP5CypYcbvtpSaOAXwNdD86b4FvSSbY/3ab3JKB4cA68WNIRLLkE/PpykoB6dTXOI4cCLydt8t2OlOmvQdsOwDzbj0l6H7Al8PXSLkaStiJlf9tdnoq6yABI2h34PH3aasoY3kZarawOSeOBbwNr295U0mbAnhW43CzKj49LWpe0qbFYGVwHVgL+ZvsMSWtJ2sD2PaVFUWnMESw7EZx3h7nZoeJC+pdClNxxjqSdgDOB35JuYutJOqCCrOGLgb1z/dxibP8j33xLsivw6baxyR3GSjAN+CGpJOJDpAnOg0UVJWrVBSkw3xqYbXtnSRsDnyusqeHbwOZ5BekI4HukpfRBV3G6wDSSY0s1riMtfB3YG1hQ4WrNs6QywqupqIww813SZ3oagO35ks6hvMvNJXlT75dJe5BMStgUR9KxwFYk15YzSPu3zgZ2KKkrU2XMESw7EZx3hzGkjEnrUlxpOyiArwK72b4LFmdPzgVeXVKU7c8Ocq5INmAp9nbXldDUgRfZ/p6kQ3Om/xpJNWT0a9UF8ITtJyQhaXnbd2ZbxRp4xrYlvRX4Rn4PO5aidZkHbV+89H8rwu+AWysMzCEFSheWFjEAK9meI7Vub6C4s4ftz+en50u6BFihlj4EpBrzV5EmDdj+Q3ZsqYFaY45gGYngvAvYPrC0hgEY3QTmALZ/JWl0SUEVcw5wGZXa22WakqQ/ZoeDP5DKNUpTqy6A+3Nm7kLgSkkPk/TVwEJJR5Gcbl6bLR9r+H0eK+l0ksNNbVm5I4BL8+SvVVvxxjC17OUZgL9IGkfeqCrpHVTgSCVp/w5jVTR9A57Kk+fmPVu5tKAWRgGHtliKrkFKxgXDhAjOu4CkFYCDWLLm9gPFRCXmSmqWyiEFATcW1FMtOVvzKLBvDpLWJv1+VpG0Suk64Mzx2SP7k8AppOzJf5SVBNSrC9t75afH5XKD1YCfF5TUyj4kx4yDbD8gaSxpeb80BwIbkyYKTVlLLVm5E4C/k66zLyyspR+S7qFDN07bNbi1fBT4DrCxpN8D95DuB6XZuuX5CsAbSJnqGoLzH0k6DVg9W2R+gFQeVAObNYE5gO2HJUV30GFEuLV0AUnnAXeSbrRTSN3X7rB9aGFdy5MuypNINeczgG/V1pSoJiR9DDiO5Ne9ODCpYTNcsOxIWnOw86VXQ/IE8PIaGvu0I2mB7VeW1tEJSXNtb1VaRyey33/DCsA7gTUHK+PrNjn7O6pprFMbeZI/tRLrWiTtSiodEen3emVhSQBIugXYyfbD+XhN4Jpaf7fBkkRw3gUk3Wz7VS12UKNJP+TijhAN+cf7ctvzl/rPI5hsUbWtc+vyGpB0Ch0ycg2lNpzVqgv6ZTFFaibycH6+OnCf7eKOEErtt/erqMYWAEnfBb5m+/bSWtqRdCLwC9tXlNayLEiaaXtSBTrWBr4ArGt7sqSJwPa2v1dYWj/yvXO+7X8vrKPayTMsLgc6Cvgx6Tr3LuAE21MHfWFQDVHW0h2amttHJG0KPEDhTmIAkqaTuiMuR7KRe1DSNbYPKyqsbn5HKm+pibn5cQdgIskZBVJmrmSZUq26aILv3OTkYtuX5uPJQC033CeABUrdLh9rBitw95gEHJAnOE/SZ1dYw+rRR4EjJD1FavpTjZVi9sRuGEVy+qhlA+EPSI4jR+fjX5F+r0WDc/U1f4P0nk0EflROUcL2s5Iel7RabZNnANtnSZpLsoQVyf2susl0MDCROe8C2Uv5fOCVpIvgKsAxtk8rrKvJ6B8MrGf72Ca7X1JXzeQa/QnAz6hsw1mumd6t8arPWaYrbO8cujoj6Ubbr24bq6I0YiBnltIbCyWt32m83fo06E/+HTQ33GdIFrZfsf2rYqIykn5pe+vmnpDH5tneorCuVtvQZ4B73b/LcDGUulZvB9Q2eQ56gMicd4ercu3XDHKrZknFl82B5SS9lLTkdfTS/jkA4L78N5o6nDNaWZeUiWvqpVfJY6WpVRckl4rPkPyJTdoEV0XJklOTsBWBsa2uSqWxfa+kScBGTfMV0mdaHCUvwPcCG9j+vKT1gJfarqHl+2Tg7aRV0+be+27SPqTSPJZr4hvnke2oY4VwLrAo97gYD2wp6U+uoFkeKUHzs9Iigt4kgvPucD6pu18rP6awnzjppnA5MNP2LyVtSOrAGQzMpcB/0v8Ga+q4wZ4I3JwzdJCa1RxXTs5iOumqpdHPvsCxwE/y8Yw8VhxJewBfIbmObCBpC2BK6c1wqrv5yrdIG7VfT+oU+nfgv+nv+lGKC4FHSG4jTxTW0s5hwMXAOEmzgLWAd5SVBKTf447ZCvAqUrC+D2kCVpRaJ89BbxBlLUOIUrfBTYAvkbqvNYwhtaTfpIiw4Dkj6S7gU8CttHRHrGVJX9I6wLb58AbbD5TU01CrrpqRdCMpyJzeUmpQ3ClF0jxy85UWXVWUw0m6yfaWbeUZt9jevAJtt9retLSOgZC0HGnCJeCuGrLTLZ/nx4EVbX+p9bMtrG3x5Nl2NZPnoDeIzPnQMoHUsnx1YI+W8YXAB4soaqFi//WaedD2T0uLaEXSxk6dLZvVmd/lx3UlrWv7plLaACRNyXZxF+XjUZKm2S6e/WrbcNbwKClDd5rtkhnOZ2w/qv5dG2vIptTcfOXp7KTRaFuLlkl0Ya6T9ErbC0oLaSffCz5C2uxr4FpJpxb+/kOqVNqelCk/KI/VErccB2wDTAewPa+SctWgB6jlS96T2L4IuEjS9ravL62nA1NJ/utvpMV/vaii+qmxO+InSZO9Th3gTMq+lmSspKNsf1HJW/88csvrCribtIR/bj7eh+RhP57UUGS/QroAbpX0HuAFkjYCDgGuK6inoebmK98klSi9RNIJpNKMY0oKkrSA9DtcDjhQ0t3U53JzFilpdEo+3pd0f3hnMUWJQ0mWgD+xfVsuvbx6Ka/pFrVOnoMeIMpauoCkLwHHA4tI3Qc3Bz5h++zCuqr3X68NSWeTuiPeRv8mRLHaMAB5k940YAGwM3CZ7a+VVZWQNMP2azuNSbqtZOmZpJVIG7UXNzkBPl86mynpJOB/23TtYvvTJXU15HLCN5C0XWW7aMJhIHebhhpK4jqV/tRSDlQr2bnrKuBI0kbfQ4DRtj9UVFjQE0Rw3gUaSypJewFvI7Uuv7r0hU/SHNvbSJpBWtJ8AJjjOtpJV0kNNb/tSNp7sPOlsvptvs6jgdOAWWTv5NLlNgCS7gDeaPu+fDwW+LntibXUttZGUwfcNlZLzflU2/stbSzoj6QfAKfanp2PtwUOsP2RwrrWAo5gydLL4gmktskz9E2eo8N28C8TZS3dobHcezNwru2H2pbCSvGdvAv+GNJO/VWAalpJV8psSRMra+iwxyDnDJQquWkvs3mY1ETkq9RRbgOpJGimpN+QMq0bAB/JddSl/cTHkzYfv4KWa3WpwETSh0mT+A0ltXYSXpU06aqBfisdeZNjaVes4cC2wP6S7svHY4E7mpKcghOvaaRmSLsDHwIOAB4spKWdt9g+mhYbYknvJJXtBcG/RGTOu4BSS+m3kcpatiFtEL3E9raDvjCojpxpHQfU2B0xeA7kOviNSZ/lnaXLRhok3QKcSuqm+mwzbrtId1VJqwFrAF8kLeU3LLT9UOdXdQdJR5EsTlcEHm859TTwHdtHFRE2TKi19Ea5SVjryoxSF+vXLe21XdDWaQVpibEgeC5EcN4lcob6b05tf1cCxpS2k5O0NvAFYF3bkyVNBLa3XbRlc80MdBOrpG50NZJnd1NDfQ3J2qtoM5Hav2eSXsOS2emzignKqEP30mBwJH2RZF07nr4yCNueUU5V/UgaB9xv+0lJOwGbAWfZfqSwrtm2t5N0OWmz7x+AH9seV1DTZNIq+LtIWf2GMcBE29sUERb0FBGcd4kaAwBJl5GaiBxte/O8BHxzbTXVwbIh6XyS/3pTjrEfsLntQWvSh5qav2eSppJWQubRl522C7bglrRmfnoI8GeS+0irM1DRLHXNZPeYQ4CXkz7T7YDra6hRrhkl7/qtSPeoy0lljhNsv7mwrt2Ba4H1SE4yY4DjStrZStoc2ILkcNZaBrqQtJfs4SLCgp4igvMuUGMAACDpl7a3Vv+GHfNsb1FSV/Dc6PTZ1fB51vw9y2VKE13RhVDSPaSa/E4bUxwbtgcm10hvDczOm/A3Bj5ne5/C0qpGfc1+jgAW2T6lhg3Rks4EDm0y+Hni+pUa3LEkjXZu1JRXxtezPX8pLwuCZSI2hHaHragsAMg8JulF9DXs2I7UgCUYniySNMn2TABJO5D2OZSm5u/ZrcA6wB9LC2mwvQGkxjDt9e9KzWKCgXnC9hOSkLS8U3OuCaVFDQOelrQvsD99G8xHD/L/3WKz1tKabKZQi4PSlZL2JMVR84AHcz38YYV1BT1ABOfdoboAIHMYaflynKRZpGYs7ygrKfgX+DBwZq49h+SOckBBPQ01f89eDNwuaQ79S0dqaMF9HdC+uazTWNDH/ZJWBy4kBU8Pk+qUg8E5kOSGcoLte5Q6XRbtw5EZJWmNplQkZ85riVtWs/03SQcDZ9g+ts3FKAieM7V8yXudWgOAccBkUj3f20l2WvGdGL7cQdoMN47kCPQoySWo6A3D9k2SXgdMIJVq3NUsB1fAcaUFtCNpHeBlwIo5S9iUt4wBViombBhge6/89DhJVwOrkRq/BYOQrWEPaTm+BzixnKLFfBW4TtKPSStv7wJOKCtpMctJeilJ09FL++cg+GeIQKw7HFdawAAcY/u8XC+3C+lC+G1SkB4MPy4CHgFuAn5fWMtisjvRYcD6tj8oaSNJE2xfUlqb7WtKa+jAG4H3kzY1ntwyvpBkFxgsA5V+tlXR+JgPdL60RaztsyTNJfVEELB3RT0mppA2z860/UtJGwK/Lqwp6BFiQ+gIptnwk+3HFtg+p4ZNQMFzQ9KttjctraMdST8keXXvb3tTSSuSHDSKbQiVNNP2JEkL6R+cNL71YwpJ6xMivd32+aV1BL1LizXsR/Pj1Pz4XuBx21O6ryoIggjOh5DaAwBJl5AyrLuQuugtAubY3rykruC5Iek7wCm2F5TW0oqkuba3anNruSW+Z0tH0ltYsnV5BEzB84qkWbZ3WNpYAJKOsP0lSafQYdWhtAtb0BtEWcsQYntSfly1tJYBeBfwJpI11SO5fu7wwpqCf5KWpenlgAMl3U1d3Uufytnyxq1lHC17L0oi6aD2ZkiSTrR95ECv6RaSTiXVmO8MnE7aRDunqKigV1m5zenpNcDKhTXVyh35cW5RFUFPE5nzIBjm1Np6u0HSrsBngInAFcAOwPttTy+pCxY3SDrb9rR8/C1ghUp8lOfb3qzlcRXgAtu7ldYW9BaSXg18n7SBFtLelQ/YvqmcqiAYuURwHgTBkJKbcC0glU3dDdxg+y9lVSVyRv9iUmAyGXjI9ifKqkpIusH2tpJmA3sDfwVutb1RYWlBjyJpDCkuqKUPQbVI+ilLlrU8Ssqon9beoyAI/hmirCUIgqHmDGASsCuwITBP0gzb3yglKPslNxxM8sWeBUyRtKbth8oo68cl2bP7yyQHHpPKW4LgeUXS8iQ73VeQLAKB2N+wFO4m9Ww4Nx/vA/wJGA98F9ivkK6gB4jMeRAEQ46kF5Daqu9ManayyPbGBfXcw5KbtBtse8MuSxqUHDytEBnNYCiQ9HNS1vdG4Nlm3PZXi4mqnJxgeG2nMUm32d6klLZg+BOZ8yAIhhRJV5E2l10PXAtsbfvPJTXZ3kDSKGB727NKahmI7A//SWBs9ocfK2nHGvzhg57j5bbfVFrEMGMtSWNt3wcgaSyp4SDAU+VkBb3AqNICgiDoeeaTblabApsBjdd5UWz/A/hKaR2DcAbJ1Wb7fHw/cHw5OUEPc52kV5YWMcz4JDBT0tWSppMSD4dLWhk4s6iyYNgTZS1BEHSF7DZyIPApYB3byxeWhKTPkSYPF7iyi2H4wwfdQtLtwL8B91CXDWvV5HKzjUnv152xCTR4voiyliAIhhRJHwN2JDW6upfkjHJtUVF9HEYquXlW0iIqaRCWqdYfPug5JpcWMNzIZWeHAevnsrONJE2IsrPg+SCC8yAIhpoVgZOBG20/U1pMKxU3CAM4Fvg5sJ6kaWR/+KKKgp5C0hjbfwMWltYyDDmDtIG2tezsPCCC8+BfJspagiAY0UjaE2hcF6bXkvmq2R8+6A0kXWJ79xb3oqpdi2oiys6CoSQy50EQjFgknUiyeJyWhw7NbcyPLCiroTp/+KC3sL17fjoTmAFca/vOgpKGE1F2FgwZkTkPgmDEImk+sEV2bmn82G+uZSNcbf7wQW8i6fWkieCOpIngzaRAPSaCHVDq0rQfcBAwEbiCXHZme3pBaUGPEMF5EAQjlhyc79R0BM2dQ6fXEJx38IefWdofPuhdYiL4zyHpRmA3YDtSOdDsKDsLni+irCUIgpHMF4Cbsk+xSLXnRxVV1Md8ksPNpqTujY9Iut72orKygl6jxkZhw4DZwIa2f1ZaSNB7ROY8CIIRS950+WvgYeA+0qbLB8qq6k+N/vBBbyHpa6SJ4JPALFL9eUwEByF7w48n2cM+RnjDB88jEZwHQTBi6VBrOw+oYtNlB3/4ZsPeL4oKC3qWmAguO5LW7zRu+95uawl6jwjOgyAY0dRaayvpcFJAXp0/fNBbxEQwCOoigvMgCEYssekyCGIiGAS1ERtCgyAYycSmy2DEY/vLpTUEQdBHZM6DIBjxRK1tEARBUAuROQ+CYMTSodb2+6TyliAIgiAoQgTnQRCMZFYETiZqbYMgCIJKiLKWIAiCIAiCIKiEUaUFBEFWO8BeAAAAOklEQVQQBEEQBEGQiOA8CIIgCIIgCCohgvMgCIIgCIIgqIQIzoMgCIIgCIKgEiI4D4IgCIIgCIJK+H8WSWUOR7196AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = data.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "day.csv为原始数据，day-train。csv为训练数据，day-test。csv为测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入训练数据\n",
    "data_train = data[data['yr'] == 0]\n",
    "#导入测试数据\n",
    "data_test = data[data['yr'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从训练数据中分离输入特征x和输出y\n",
    "y_train = data_train['cnt'].values\n",
    "X_train = data_train.drop(['yr','dteday','instant','cnt','casual','registered'], axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns_train = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 从测试数据中分离输入特征x和输出y\n",
    "y_test = data_test['cnt'].values\n",
    "X_test = data_test.drop(['yr','dteday','instant','cnt','casual','registered'], axis = 1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns_test = X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 训练数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "#对y进行标准化\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺省参数线性回归尝试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns_train</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.6552364353084585]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>[0.2951780426792696]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.03533240218145266]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.007286281574697691]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mnth</td>\n",
       "      <td>[0.0038685715616471896]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[-0.02114072759985185]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.03601989254948183]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.05942477581033442]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.1239185474233161]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>[-0.22569477586902076]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  columns_train                     coef\n",
       "6          temp     [0.6552364353084585]\n",
       "0        season     [0.2951780426792696]\n",
       "3       weekday    [0.03533240218145266]\n",
       "4    workingday   [0.007286281574697691]\n",
       "1          mnth  [0.0038685715616471896]\n",
       "7         atemp   [-0.02114072759985185]\n",
       "2       holiday   [-0.03601989254948183]\n",
       "8           hum   [-0.05942477581033442]\n",
       "9     windspeed    [-0.1239185474233161]\n",
       "5    weathersit   [-0.22569477586902076]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns_train\":list(columns_train), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评价模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is -0.6965661915185128\n",
      "The r2 score of LinearRegression on train is 0.758519666910396\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRC9RrSETEqIu6IiLsi4r6I+Hy1fK+IuD0ilkbE1RHxkoEvV5Kk5lFPT/o54E2ZeQgwCTg6IqYCXwW+kZn7AH8BThu4MiVJaj69hnTWrK0etlS3BN4EXFctvww4fkAqlCSpSY2sZ6OIGAEsAl4JzAH+ADyRmeuqTZYDe/bw3NnAbIAJEyb0t15JWq+9vbHbSaWpa+BYZnZk5iRgPDAF2K+7zXp47tzMbMvMttbW1r5XKklSk9mi0d2Z+QQwH5gK7BgRnT3x8cCjjS1NkqTmVs/o7taI2LG6/1LgzcAS4BbgXdVms4AbBqpISZKaUT3XpMcBl1XXpbcBrsnMn0XE/cBVEfFF4L+A7w9gnZIkNZ1eQzoz7wYmd7P8QWrXpyVJ0gBwxjFJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFaqeb8GSpEHV3j7UFUhlsCctSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqFGDnUBkgZXe3tjt5M0cOxJS5JUKENakqRCGdKSJBXKkJYkqVC9hnREvCwibomIJRFxX0R8uFreHhErImJxdXv7wJcrSVLzqGd09zrg45l5Z0RsDyyKiF9W676RmRcOXHmSJDWvXkM6M1cCK6v7T0fEEmDPgS5MkqRmt0XXpCNiIjAZuL1adHZE3B0Rl0TETg2uTZKkplb3ZCYRMRr4V+AjmflURHwb+AKQ1c+vAad287zZwGyACRMmNKJmSYPASU+koVdXTzoiWqgF9JWZ+ROAzFyVmR2Z+SLwXWBKd8/NzLmZ2ZaZba2trY2qW5KkYa+e0d0BfB9Ykplf77J8XJfN3gnc2/jyJElqXvWc7j4cOAm4JyIWV8s+A7wvIiZRO939MHDmgFQoSVKTqmd0961AdLPqF40vR5IkdXLGMUmSCmVIS5JUKENakqRC1f05aUnaWm3JZ7n93LdKYk9akqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVKheQzoiXhYRt0TEkoi4LyI+XC3fOSJ+GRFLq587DXy5kiQ1j3p60uuAj2fmfsBU4EMRsT/waWBeZu4DzKseS5KkBuk1pDNzZWbeWd1/GlgC7AnMBC6rNrsMOH6gipQkqRlt0TXpiJgITAZuB3bLzJVQC3JgbA/PmR0RCyNi4erVq/tXrSRJTaTukI6I0cC/Ah/JzKfqfV5mzs3Mtsxsa21t7UuNkiQ1pbpCOiJaqAX0lZn5k2rxqogYV60fBzw2MCVKktSc6hndHcD3gSWZ+fUuq24EZlX3ZwE3NL48SZKa18g6tjkcOAm4JyIWV8s+A3wFuCYiTgP+H/DugSlRkqTm1GtIZ+atQPSwekZjy5EkSZ2ccUySpEIZ0pIkFcqQliSpUPUMHJOkHrW3D3UFjVVve4Zbu1Ume9KSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWrkUBcgqWft7UNdgaShZE9akqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKlSvIR0Rl0TEYxFxb5dl7RGxIiIWV7e3D2yZkiQ1n3p60pcCR3ez/BuZOam6/aKxZUmSpF5DOjMXAH8ehFokSVIXI/vx3LMj4mRgIfDxzPxLdxtFxGxgNsCECRP68XKSVI729sZuJ3WnrwPHvg28ApgErAS+1tOGmTk3M9sys621tbWPLydJUvPpU0hn5qrM7MjMF4HvAlMaW5YkSepTSEfEuC4P3wnc29O2kiSpb3q9Jh0RPwamA7tGxHLgPGB6REwCEngYOHMAa5QkqSn1GtKZ+b5uFn9/AGqRJEldOOOYJEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSokUNdgDRU2tsbu500WPy32zzsSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK5WQm0hBwkglJ9bAnLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFarXkI6ISyLisYi4t8uynSPilxGxtPq508CWKUlS86mnJ30pcPRGyz4NzMvMfYB51WNJktRAvYZ0Zi4A/rzR4pnAZdX9y4DjG1yXJElNr6+TmeyWmSsBMnNlRIztacOImA3MBpgwYUIfX06Stk71TlzjBDfqzoAPHMvMuZnZlpltra2tA/1ykiQNG30N6VURMQ6g+vlY40qSJEnQ95C+EZhV3Z8F3NCYciRJUqd6PoL1Y+A3wKsiYnlEnAZ8BXhLRCwF3lI9liRJDdTrwLHMfF8Pq2Y0uBZJktSFM45JklQoQ1qSpEIZ0pIkFaqvk5lIkoYJJ1wplz1pSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqGczERqICd7kNRI9qQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhXIyE6kXTlAiaajYk5YkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQvk5aUkapvyM/9bPnrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSofo3ujoiHgaeBDmBdZrY1oihJktSYj2C9MTMfb8B+JElSF57uliSpUP3tSSdwc0Qk8J3MnLvxBhExG5gNMGHChH6+nCQNT048ou70tyd9eGYeCrwN+FBEHLnxBpk5NzPbMrOttbW1ny8nSVLz6FdIZ+aj1c/HgOuBKY0oSpIk9SOkI2K7iNi+8z7wVuDeRhUmSVKz68816d2A6yOicz8/ysz/3ZCqJElS30M6Mx8EDmlgLZIkqQs/giVJUqEMaUmSCmVIS5JUqEZMCyr12UBM4OCkENLQq/f30N/XzbMnLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUk5lo2HFyBGlg+Ls1+OxJS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrlZCYFqXeigEZvt6XbSpIGhz1pSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqG26slMhnICDl9bkgZPs/6/Z09akqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqVL9COiKOjojfRcSyiPh0o4qSJEn9COmIGAHMAd4G7A+8LyL2b1RhkiQ1u/70pKcAyzLzwcx8HrgKmNmYsiRJUmRm354Y8S7g6Mw8vXp8EvDazDx7o+1mA7Orh68CftfN7nYFHu9TIVs/296cbHvzaub22/a/e3lmtvb2pP7MOBbdLNsk8TNzLjB3szuKWJiZbf2oZatl2217s2nmtkNzt9+2b3nb+3O6eznwsi6PxwOP9mN/kiSpi/6E9H8C+0TEXhHxEuC9wI2NKUuSJPX5dHdmrouIs4F/B0YAl2TmfX3c3WZPhw9ztr052fbm1cztt+1bqM8DxyRJ0sByxjFJkgplSEuSVKghCemIuCAiHoiIuyPi+ojYsYftht20oxHx7oi4LyJejIgeh+NHxMMRcU9ELI6IhYNZ40DZgrYPx+O+c0T8MiKWVj936mG7juqYL46IrXogZm/HMSK2jYirq/W3R8TEwa9yYNTR9lMiYnWXY336UNQ5ECLikoh4LCLu7WF9RMRF1Xtzd0QcOtg1DpQ62j49Ip7sctw/1+tOM3PQb8BbgZHV/a8CX+1mmxHAH4C9gZcAdwH7D0W9DW77ftQmdZkPtG1mu4eBXYe63sFu+zA+7v8D+HR1/9Pd/Zuv1q0d6lob1N5ejyPwQeB/VfffC1w91HUPYttPAS4e6loHqP1HAocC9/aw/u3ATdTm2pgK3D7UNQ9i26cDP9uSfQ5JTzozb87MddXD31L7jPXGhuW0o5m5JDO7m3Vt2Kuz7cPyuFNrw2XV/cuA44ewlsFQz3Hs+p5cB8yIiO4mSdraDNd/w3XJzAXAnzezyUzg8qz5LbBjRIwbnOoGVh1t32IlXJM+ldpfVRvbE/hjl8fLq2XNIoGbI2JRNbVqsxiux323zFwJUP0c28N2oyJiYUT8NiK25iCv5ziu36b6o/1JYJdBqW5g1ftv+B+r073XRcTLulk/XA3X3/F6TYuIuyLipog4oLeN+zMt6GZFxP8Bdu9m1bmZeUO1zbnAOuDK7nbRzbKt4vNi9bS9Dodn5qMRMRb4ZUQ8UP2VVrQGtH1YHvct2M2E6rjvDfwqIu7JzD80psJBVc9x3GqPdS/qade/AT/OzOci4gPUzii8acArK8NwPe71uJPanN1rI+LtwE+BfTb3hAEL6cx88+bWR8Qs4FhgRlYn6zey1U472lvb69zHo9XPxyLiemqn0IoP6Qa0fVge94hYFRHjMnNldWrvsR720XncH4yI+cBkatc3tzb1HMfObZZHxEhgDA0+VThEem17Zq7p8vC71MbmNIut9ne8vzLzqS73fxER34qIXTOzxy8dGarR3UcDnwKOy8y/9rBZ0047GhHbRcT2nfepDbTrdrTgMDRcj/uNwKzq/ixgk7MKEbFTRGxb3d8VOBy4f9AqbKx6jmPX9+RdwK96+IN9a9Nr2ze6BnscsGQQ6xtqNwInV6O8pwJPdl4KGu4iYvfOcRcRMYVaBq/Z7JOGaATcMmrXJBZXt84RnnsAv+iy3duB31PrSZw7FLUOQNvfSe0vyeeAVcC/b9x2aqNC76pu9zVT24fxcd8FmAcsrX7uXC1vA75X3X8dcE913O8BThvquvvZ5k2OI3A+tT/OAUYB11b/H9wB7D3UNQ9i279c/W7fBdwCvHqoa25g238MrAReqH7fTwM+AHygWh/AnOq9uYfNfMpla7vV0fazuxz33wKv622fTgsqSVKhShjdLUmSumFIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkq1P8H0j0cAMNkbTkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化观察残差分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True biker num')\n",
    "plt.ylabel('Predicted biker num')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.7011352437314473\n",
      "The r2 score of RidgeCV on train is 0.757697549242605\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.14527588617252218\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns_train</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.6552364353084585]</td>\n",
       "      <td>[0.35015384686244255]</td>\n",
       "      <td>0.067640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>[0.2951780426792696]</td>\n",
       "      <td>[0.275765425136457]</td>\n",
       "      <td>0.193429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.03533240218145266]</td>\n",
       "      <td>[0.033998994978232]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.007286281574697691]</td>\n",
       "      <td>[0.00729342301538799]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mnth</td>\n",
       "      <td>[0.0038685715616471896]</td>\n",
       "      <td>[0.01883600695545369]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[-0.02114072759985185]</td>\n",
       "      <td>[0.2804709635627445]</td>\n",
       "      <td>0.476180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.03601989254948183]</td>\n",
       "      <td>[-0.03364170993901783]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.05942477581033442]</td>\n",
       "      <td>[-0.06294976734685775]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.1239185474233161]</td>\n",
       "      <td>[-0.11713890245230177]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>[-0.22569477586902076]</td>\n",
       "      <td>[-0.21642688270722443]</td>\n",
       "      <td>-0.127561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  columns_train                  coef_lr              coef_ridge  coef_lasso\n",
       "6          temp     [0.6552364353084585]   [0.35015384686244255]    0.067640\n",
       "0        season     [0.2951780426792696]     [0.275765425136457]    0.193429\n",
       "3       weekday    [0.03533240218145266]     [0.033998994978232]    0.000000\n",
       "4    workingday   [0.007286281574697691]   [0.00729342301538799]    0.000000\n",
       "1          mnth  [0.0038685715616471896]   [0.01883600695545369]    0.000000\n",
       "7         atemp   [-0.02114072759985185]    [0.2804709635627445]    0.476180\n",
       "2       holiday   [-0.03601989254948183]  [-0.03364170993901783]   -0.000000\n",
       "8           hum   [-0.05942477581033442]  [-0.06294976734685775]   -0.000000\n",
       "9     windspeed    [-0.1239185474233161]  [-0.11713890245230177]   -0.000000\n",
       "5    weathersit   [-0.22569477586902076]  [-0.21642688270722443]   -0.127561"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可视化\n",
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns_train\":list(columns_train), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.863855024061922\n",
      "The r2 score of LassoCV on train is 0.692374913339408\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)\n",
    "lasso = LassoCV()\n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 10.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns_train</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.6552364353084585]</td>\n",
       "      <td>[0.35015384686244255]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>[0.2951780426792696]</td>\n",
       "      <td>[0.275765425136457]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>weekday</td>\n",
       "      <td>[0.03533240218145266]</td>\n",
       "      <td>[0.033998994978232]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.007286281574697691]</td>\n",
       "      <td>[0.00729342301538799]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mnth</td>\n",
       "      <td>[0.0038685715616471896]</td>\n",
       "      <td>[0.01883600695545369]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[-0.02114072759985185]</td>\n",
       "      <td>[0.2804709635627445]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.03601989254948183]</td>\n",
       "      <td>[-0.03364170993901783]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.05942477581033442]</td>\n",
       "      <td>[-0.06294976734685775]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.1239185474233161]</td>\n",
       "      <td>[-0.11713890245230177]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>[-0.22569477586902076]</td>\n",
       "      <td>[-0.21642688270722443]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  columns_train                  coef_lr              coef_ridge\n",
       "6          temp     [0.6552364353084585]   [0.35015384686244255]\n",
       "0        season     [0.2951780426792696]     [0.275765425136457]\n",
       "3       weekday    [0.03533240218145266]     [0.033998994978232]\n",
       "4    workingday   [0.007286281574697691]   [0.00729342301538799]\n",
       "1          mnth  [0.0038685715616471896]   [0.01883600695545369]\n",
       "7         atemp   [-0.02114072759985185]    [0.2804709635627445]\n",
       "2       holiday   [-0.03601989254948183]  [-0.03364170993901783]\n",
       "8           hum   [-0.05942477581033442]  [-0.06294976734685775]\n",
       "9     windspeed    [-0.1239185474233161]  [-0.11713890245230177]\n",
       "5    weathersit   [-0.22569477586902076]  [-0.21642688270722443]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可视化\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns_train\":list(columns_train), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  }
 ],
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